Trinity Devboard PCB V1.0 Firmware. FreeRTOS is setup and the MCU reads IMU data over SPI fand Magnetometer data over I2C, each with a seperate task. Sensordata is then run though MadgwickAHRS and send over USB as serial packet data to use in trinity visualizer. Bare minimum functionality works and is replicated from the first prototype.

Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q15.c

@@ -0,0 +1,101 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_activations_q15.c
+ * Description:  Q15 neural network activation function using direct table look-up
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_common_tables.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+  /**
+   * @brief Q15 neural network activation function using direct table look-up
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @param[in]       int_width   bit-width of the integer part, assume to be smaller than 3
+   * @param[in]       type        type of activation functions
+   * @return none.
+   *
+   * @details
+   * 
+   * This is the direct table look-up approach.
+   *
+   * Assume here the integer part of the fixed-point is <= 3.
+   * More than 3 just not making much sense, makes no difference with
+   * saturation followed by any of these activation functions. 
+   */
+
+void arm_nn_activations_direct_q15(q15_t * data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
+{
+    uint16_t  i = size;
+    q15_t    *pIn = data;
+    q15_t    *pOut = data;
+    uint16_t  shift_size = 8 + 3 - int_width;
+    uint32_t  bit_mask = 0x7FF >> int_width;
+    uint32_t  full_frac = bit_mask + 1;
+    const q15_t *lookup_table;
+
+    switch (type)
+    {
+    case ARM_SIGMOID:
+        lookup_table = sigmoidTable_q15;
+        break;
+    case ARM_TANH:
+    default:
+        lookup_table = tanhTable_q15;
+        break;
+    }
+
+    while (i)
+    {
+        q15_t     out;
+        q15_t     in = *pIn++;
+        q15_t     frac = (uint32_t) in & bit_mask;
+        q15_t     value = lookup_table[__USAT(in >> shift_size, 8)];
+        q15_t     value2 = lookup_table[__USAT(1 + (in >> shift_size), 8)];
+
+        /* doing the interpolation here for better accuracy */
+        out = ((q31_t) (full_frac - frac) * value + (q31_t) value2 * frac) >> shift_size;
+
+        *pOut++ = out;
+        i--;
+    }
+
+}
+
+/**
+ * @} end of Acti group
+ */

Drivers/CMSIS/NN/Source/ActivationFunctions/arm_nn_activations_q7.c

@@ -0,0 +1,91 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_activations_q7.c
+ * Description:  Q7 neural network activation function using direct table look-up
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_common_tables.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+  /**
+   * @brief Q7 neural network activation function using direct table look-up
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @param[in]       int_width   bit-width of the integer part, assume to be smaller than 3
+   * @param[in]       type        type of activation functions
+   * @return none.
+   *
+   * @details
+   * 
+   * This is the direct table look-up approach.
+   *
+   * Assume here the integer part of the fixed-point is <= 3.
+   * More than 3 just not making much sense, makes no difference with
+   * saturation followed by any of these activation functions. 
+   */
+
+void arm_nn_activations_direct_q7(q7_t * data, uint16_t size, uint16_t int_width, arm_nn_activation_type type)
+{
+    uint16_t  i = size;
+    q7_t     *pIn = data;
+    q7_t     *pOut = data;
+    q7_t      in;
+    q7_t      out;
+    uint16_t  shift_size = 3 - int_width;
+    const q7_t *lookup_table;
+    switch (type)
+    {
+    case ARM_SIGMOID:
+        lookup_table = sigmoidTable_q7;
+        break;
+    case ARM_TANH:
+    default:
+        lookup_table = tanhTable_q7;
+        break;
+    }
+    while (i)
+    {
+        in = *pIn++;
+        out = lookup_table[(uint8_t) (in >> shift_size)];
+        *pOut++ = out;
+        i--;
+    }
+}
+
+/**
+ * @} end of Acti group
+ */

Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q15.c

@@ -0,0 +1,106 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_relu_q15.c
+ * Description:  Q15 version of ReLU
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+  /**
+   * @brief Q15 RELU function
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @return none.
+   * 
+   * @details
+   *
+   * Optimized relu with QSUB instructions.
+   *
+   */
+
+void arm_relu_q15(q15_t * data, uint16_t size)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    uint16_t  i = size >> 1;
+    q15_t    *pIn = data;
+    q15_t    *pOut = data;
+    q31_t     in;
+    q31_t     buf;
+    q31_t     mask;
+
+    while (i)
+    {
+        in = *__SIMD32(pIn)++;
+
+        /* extract the first bit */
+        buf = __ROR(in & 0x80008000, 15);
+
+        /* if MSB=1, mask will be 0xFF, 0x0 otherwise */
+        mask = __QSUB16(0x00000000, buf);
+
+        *__SIMD32(pOut)++ = in & (~mask);
+        i--;
+    }
+
+    if (size & 0x1)
+    {
+        if (*pIn < 0)
+        {
+            *pIn = 0;
+        }
+        pIn++;
+    }
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  i;
+
+    for (i = 0; i < size; i++)
+    {
+        if (data[i] < 0)
+            data[i] = 0;
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+}
+
+/**
+ * @} end of Acti group
+ */

Drivers/CMSIS/NN/Source/ActivationFunctions/arm_relu_q7.c

@@ -0,0 +1,110 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_relu_q7.c
+ * Description:  Q7 version of ReLU
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Acti
+ * @{
+ */
+
+  /**
+   * @brief Q7 RELU function
+   * @param[in,out]   data        pointer to input
+   * @param[in]       size        number of elements
+   * @return none.
+   * 
+   * @details
+   *
+   * Optimized relu with QSUB instructions.
+   *
+   */
+
+void arm_relu_q7(q7_t * data, uint16_t size)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    uint16_t  i = size >> 2;
+    q7_t     *pIn = data;
+    q7_t     *pOut = data;
+    q31_t     in;
+    q31_t     buf;
+    q31_t     mask;
+
+    while (i)
+    {
+        in = *__SIMD32(pIn)++;
+
+        /* extract the first bit */
+        buf = __ROR(in & 0x80808080, 7);
+
+        /* if MSB=1, mask will be 0xFF, 0x0 otherwise */
+        mask = __QSUB8(0x00000000, buf);
+
+        *__SIMD32(pOut)++ = in & (~mask);
+        i--;
+    }
+
+    i = size & 0x3;
+    while (i)
+    {
+        if (*pIn < 0)
+        {
+            *pIn = 0;
+        }
+        pIn++;
+        i--;
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    uint16_t  i;
+
+    for (i = 0; i < size; i++)
+    {
+        if (data[i] < 0)
+            data[i] = 0;
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+}
+
+/**
+ * @} end of Acti group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_HWC_q7_fast_nonsquare.c

@@ -0,0 +1,235 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_1x1_HWC_q7_fast_nonsquare.c
+ * Description:  Fast Q7 version of 1x1 convolution (non-square shape)
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Fast Q7 version of 1x1 convolution (non-sqaure shape)
+ * @param[in]       Im_in        pointer to input tensor
+ * @param[in]       dim_im_in_x  input tensor dimention x
+ * @param[in]       dim_im_in_y  input tensor dimention y
+ * @param[in]       ch_im_in     number of input tensor channels
+ * @param[in]       wt           pointer to kernel weights
+ * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+ * @param[in]       dim_kernel_x filter kernel size x
+ * @param[in]       dim_kernel_y filter kernel size y
+ * @param[in]       padding_x    padding size x
+ * @param[in]       padding_y    padding size y
+ * @param[in]       stride_x     convolution stride x
+ * @param[in]       stride_y     convolution stride y
+ * @param[in]       bias         pointer to bias
+ * @param[in]       bias_shift   amount of left-shift for bias
+ * @param[in]       out_shift    amount of right-shift for output
+ * @param[in,out]   Im_out       pointer to output tensor
+ * @param[in]       dim_im_out_x output tensor dimension x
+ * @param[in]       dim_im_out_y output tensor dimension y
+ * @param[in,out]   bufferA      pointer to buffer space for input 
+ * @param[in,out]   bufferB      pointer to buffer space for output
+ * @return     The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is optimized for convolution with 1x1 kernel size (i.e., dim_kernel_x=1
+ * and dim_kernel_y=1). It can be used for the second half of MobileNets [1] after depthwise 
+ * separable convolution.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ *   ch_im_in is multiple of 4
+ *   ch_im_out is multiple of 2
+ *
+ * [1] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
+ * https://arxiv.org/abs/1704.04861
+ */
+
+arm_status arm_convolve_1x1_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+                                                  const uint16_t dim_im_in_x,
+                                                  const uint16_t dim_im_in_y,
+                                                  const uint16_t ch_im_in,
+                                                  const q7_t * wt,
+                                                  const uint16_t ch_im_out,
+                                                  const uint16_t dim_kernel_x,
+                                                  const uint16_t dim_kernel_y,
+                                                  const uint16_t padding_x,
+                                                  const uint16_t padding_y,
+                                                  const uint16_t stride_x,
+                                                  const uint16_t stride_y,
+                                                  const q7_t * bias,
+                                                  const uint16_t bias_shift,
+                                                  const uint16_t out_shift,
+                                                  q7_t * Im_out,
+                                                  const uint16_t dim_im_out_x,
+                                                  const uint16_t dim_im_out_y, 
+                                                  q15_t * bufferA, 
+                                                  q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x;
+    int16_t   i_ch_out;
+
+    /* -----------------------
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1
+        || padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_out_y * dim_im_in_x + i_out_x) * ch_im_in, pBuffer,
+                                             ch_im_in);
+            pBuffer += ch_im_in;
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* check if there is left-over for compute */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+        {
+            q31_t     sum = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
+            q15_t    *pB = bufferA;
+            /* basically each time it process 4 entries */
+            uint16_t  colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2;
+
+            while (colCnt)
+            {
+
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (const q7_t *)read_and_pad_reordered((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut = (q7_t) __SSAT((sum >> out_shift), 8);
+            pOut++;
+
+        }
+
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+		
+    int       i, j, k, l, m, n;
+    int       conv_out;
+    int       in_row, in_col;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0 || dim_kernel_x != 1 || dim_kernel_y != 1
+        || padding_x != 0 || padding_y != 0 || stride_x != 1 || stride_y != 1)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out_y; j++)
+        {
+            for (k = 0; k < dim_im_out_x; k++)
+            {
+                conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel_y; m++)
+                {
+                    for (n = 0; n < dim_kernel_x; n++)
+                    {
+                        // if-for implementation
+                        in_row = stride_y * j + m - padding_y;
+                        in_col = stride_x * k + n - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] *
+                                    wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_y + n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_basic.c

@@ -0,0 +1,207 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q15_basic.c
+ * Description:  Q15 version of convolution
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Basic Q15 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code> 
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * This basic version is designed to work for any input tensor and weight
+   * dimension. 
+   */
+
+arm_status
+arm_convolve_HWC_q15_basic(const q15_t * Im_in,
+                           const uint16_t dim_im_in,
+                           const uint16_t ch_im_in,
+                           const q15_t * wt,
+                           const uint16_t ch_im_out,
+                           const uint16_t dim_kernel,
+                           const uint16_t padding,
+                           const uint16_t stride,
+                           const q15_t * bias,
+                           const uint16_t bias_shift,
+                           const uint16_t out_shift,
+                           q15_t * Im_out, 
+                           const uint16_t dim_im_out, 
+                           q15_t * bufferA, 
+                           q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    uint16_t  im2col_out_pixel_index = 0;
+    q15_t    *pBuffer = bufferA;
+    q15_t    *pOut = Im_out;
+    q15_t    *im_buffer = bufferA;
+    const q15_t *pA;
+    int       i;
+
+    /* This part implements the im2col function */
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* Filling 0 for out-of-bound paddings */
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+                        memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            pA = wt;
+            for (i = 0; i < ch_im_out; i++)
+            {
+                q31_t     sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                q15_t    *pB = im_buffer;
+                uint16_t  colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
+                while (colCnt)
+                {
+                    q31_t     inA1 = *__SIMD32(pA)++;
+                    q31_t     inB1 = *__SIMD32(pB)++;
+                    q31_t     inA2 = *__SIMD32(pA)++;
+                    q31_t     inB2 = *__SIMD32(pB)++;
+
+                    sum = __SMLAD(inA1, inB1, sum);
+                    sum = __SMLAD(inA2, inB2, sum);
+
+                    colCnt--;
+                }
+                colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
+                while (colCnt)
+                {
+                    q15_t     inA1 = *pA++;
+                    q15_t     inB1 = *pB++;
+                    sum += inA1 * inB1;
+                    colCnt--;
+                }
+                *pOut = (q15_t) __SSAT((sum >> out_shift), 16);
+                pOut++;
+            }
+
+            /* counter reset */
+            pBuffer = im_buffer;
+            im2col_out_pixel_index++;
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+                conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast.c

@@ -0,0 +1,255 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q15_fast.c
+ * Description:  Fast Q15 version of convolution
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Fast Q15 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * <b>Input dimension constraints:</b>
+   *
+   * ch_im_in is multiple of 2 
+   *
+   * ch_im_out is multipe of 2
+   *
+   */
+
+arm_status
+arm_convolve_HWC_q15_fast(const q15_t * Im_in,
+                          const uint16_t dim_im_in,
+                          const uint16_t ch_im_in,
+                          const q15_t * wt,
+                          const uint16_t ch_im_out,
+                          const uint16_t dim_kernel,
+                          const uint16_t padding,
+                          const uint16_t stride,
+                          const q15_t * bias,
+                          const uint16_t bias_shift,
+                          const uint16_t out_shift,
+                          q15_t * Im_out, 
+                          const uint16_t dim_im_out, 
+                          q15_t * bufferA, 
+                          q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    q15_t    *pBuffer = bufferA;
+    q15_t    *im_buffer = bufferA;
+    q15_t    *pOut = Im_out;
+
+    if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    /* This part implements the im2col function */
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+                        memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (i_out_x & 0x1)
+            {
+                int       i;
+                /* initialize the matrix pointers for A */
+                const q15_t *pA = wt;
+
+                /* set up the second output pointers */
+                q15_t    *pOut2 = pOut + ch_im_out;
+
+                /* this loop over rows in A */
+                for (i = 0; i < ch_im_out; i += 2)
+                {
+                    /* setup pointers for B */
+                    q15_t    *pB = im_buffer;
+                    const q15_t *pB2 = pB + ch_im_in * dim_kernel * dim_kernel;
+
+                    /* aling the second pointer for A */
+                    const q15_t *pA2 = pA + ch_im_in * dim_kernel * dim_kernel;
+
+                    /* init the sum with bias */
+                    q31_t     sum =  ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+
+                    uint16_t  colCnt = ch_im_in * dim_kernel * dim_kernel >> 1;
+                    /* accumulate over the vector */
+                    while (colCnt)
+                    {
+                        q31_t     inA1 = *__SIMD32(pA)++;
+                        q31_t     inB1 = *__SIMD32(pB)++;
+                        q31_t     inA2 = *__SIMD32(pA2)++;
+                        q31_t     inB2 = *__SIMD32(pB2)++;
+
+                        sum = __SMLAD(inA1, inB1, sum);
+                        sum2 = __SMLAD(inA1, inB2, sum2);
+                        sum3 = __SMLAD(inA2, inB1, sum3);
+                        sum4 = __SMLAD(inA2, inB2, sum4);
+
+                        colCnt--;
+                    }           /* while over colCnt */
+                    colCnt = ch_im_in * dim_kernel * dim_kernel & 0x1;
+                    while (colCnt)
+                    {
+                        q15_t     inA1 = *pA++;
+                        q15_t     inB1 = *pB++;
+                        q15_t     inA2 = *pA2++;
+                        q15_t     inB2 = *pB2++;
+
+                        sum += inA1 * inB1;
+                        sum2 += inA1 * inB2;
+                        sum3 += inA2 * inB1;
+                        sum4 += inA2 * inB2;
+                        colCnt--;
+                    }           /* while over colCnt */
+                    *pOut++ = (q15_t) __SSAT(sum >> out_shift, 16);
+                    *pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16);
+                    *pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16);
+                    *pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16);
+
+                    /* skip the row computed with A2 */
+                    pA += ch_im_in * dim_kernel * dim_kernel;
+                }               /* for over ch_im_out */
+
+                pOut += ch_im_out;
+                /* counter reset */
+                pBuffer = im_buffer;
+            }
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+                conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q15_fast_nonsquare.c

@@ -0,0 +1,265 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q15_fast.c
+ * Description:  Fast Q15 version of convolution
+ *
+ * $Date:        24. May 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Fast Q15 convolution function (non-sqaure shape)
+   * @param[in]       Im_in        pointer to input tensor
+   * @param[in]       dim_im_in_x  input tensor dimention x
+   * @param[in]       dim_im_in_y  input tensor dimention y
+   * @param[in]       ch_im_in     number of input tensor channels
+   * @param[in]       wt           pointer to kernel weights
+   * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel_x filter kernel size x
+   * @param[in]       dim_kernel_y filter kernel size y
+   * @param[in]       padding_x    padding size x
+   * @param[in]       padding_y    padding size y
+   * @param[in]       stride_x     convolution stride x
+   * @param[in]       stride_y     convolution stride y
+   * @param[in]       bias         pointer to bias
+   * @param[in]       bias_shift   amount of left-shift for bias
+   * @param[in]       out_shift    amount of right-shift for output
+   * @param[in,out]   Im_out       pointer to output tensor
+   * @param[in]       dim_im_out_x output tensor dimension x
+   * @param[in]       dim_im_out_y output tensor dimension y
+   * @param[in,out]   bufferA      pointer to buffer space for input 
+   * @param[in,out]   bufferB      pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * <b>Input dimension constraints:</b>
+   *
+   * ch_im_in is multiple of 2 
+   *
+   * ch_im_out is multipe of 2
+   *
+   */
+
+arm_status
+arm_convolve_HWC_q15_fast_nonsquare(const q15_t * Im_in,
+                                    const uint16_t dim_im_in_x,
+                                    const uint16_t dim_im_in_y,
+                                    const uint16_t ch_im_in,
+                                    const q15_t * wt,
+                                    const uint16_t ch_im_out,
+                                    const uint16_t dim_kernel_x,
+                                    const uint16_t dim_kernel_y,
+                                    const uint16_t padding_x,
+                                    const uint16_t padding_y,
+                                    const uint16_t stride_x,
+                                    const uint16_t stride_y,
+                                    const q15_t * bias,
+                                    const uint16_t bias_shift,
+                                    const uint16_t out_shift,
+                                    q15_t * Im_out,
+                                    const uint16_t dim_im_out_x,
+                                    const uint16_t dim_im_out_y, 
+                                    q15_t * bufferA, 
+                                    q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    q15_t    *pBuffer = bufferA;
+    q15_t    *im_buffer = bufferA;
+    q15_t    *pOut = Im_out;
+
+    if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    /* This part implements the im2col function */
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        /* arm_copy_q15((q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+                        memcpy(pBuffer, (q15_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, sizeof(q15_t)*ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (i_out_x & 0x1)
+            {
+                int       i;
+                /* initialize the matrix pointers for A */
+                const q15_t *pA = wt;
+
+                /* set up the second output pointers */
+                q15_t    *pOut2 = pOut + ch_im_out;
+
+                /* this loop over rows in A */
+                for (i = 0; i < ch_im_out; i += 2)
+                {
+                    /* setup pointers for B */
+                    q15_t    *pB = im_buffer;
+                    const q15_t *pB2 = pB + ch_im_in * dim_kernel_y * dim_kernel_x;
+
+                    /* aling the second pointer for A */
+                    const q15_t *pA2 = pA + ch_im_in * dim_kernel_y * dim_kernel_x;
+
+                    /* init the sum with bias */
+                    q31_t     sum =  ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum2 = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum3 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+                    q31_t     sum4 = ((q31_t)bias[i + 1] << bias_shift) + NN_ROUND(out_shift);
+
+                    uint16_t  colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 1;
+                    /* accumulate over the vector */
+                    while (colCnt)
+                    {
+                        q31_t     inA1 = *__SIMD32(pA)++;
+                        q31_t     inB1 = *__SIMD32(pB)++;
+                        q31_t     inA2 = *__SIMD32(pA2)++;
+                        q31_t     inB2 = *__SIMD32(pB2)++;
+
+                        sum = __SMLAD(inA1, inB1, sum);
+                        sum2 = __SMLAD(inA1, inB2, sum2);
+                        sum3 = __SMLAD(inA2, inB1, sum3);
+                        sum4 = __SMLAD(inA2, inB2, sum4);
+
+                        colCnt--;
+                    }           /* while over colCnt */
+                    colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x1;
+                    while (colCnt)
+                    {
+                        q15_t     inA1 = *pA++;
+                        q15_t     inB1 = *pB++;
+                        q15_t     inA2 = *pA2++;
+                        q15_t     inB2 = *pB2++;
+
+                        sum += inA1 * inB1;
+                        sum2 += inA1 * inB2;
+                        sum3 += inA2 * inB1;
+                        sum4 += inA2 * inB2;
+                        colCnt--;
+                    }           /* while over colCnt */
+                    *pOut++ = (q15_t) __SSAT(sum >> out_shift, 16);
+                    *pOut++ = (q15_t) __SSAT(sum3 >> out_shift, 16);
+                    *pOut2++ = (q15_t) __SSAT(sum2 >> out_shift, 16);
+                    *pOut2++ = (q15_t) __SSAT(sum4 >> out_shift, 16);
+
+                    /* skip the row computed with A2 */
+                    pA += ch_im_in * dim_kernel_y * dim_kernel_x;
+                }               /* for over ch_im_out */
+
+                pOut += ch_im_out;
+                /* counter reset */
+                pBuffer = im_buffer;
+            }
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    if (ch_im_in % 2 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out_y; j++)
+        {
+            for (k = 0; k < dim_im_out_x; k++)
+            {
+                conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel_y; m++)
+                {
+                    for (n = 0; n < dim_kernel_x; n++)
+                    {
+                        in_row = stride_y * j + m - padding_y;
+                        in_col = stride_x * k + n - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel_x * dim_kernel_y + (m * dim_kernel_x +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q15_t) __SSAT((conv_out >> out_shift), 16);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_RGB.c

@@ -0,0 +1,279 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q7_RGB.c
+ * Description:  Q7 version of convolution for RGB image
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Q7 convolution function for RGB image
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * <b>Input dimension constraints:</b>
+   *
+   * ch_im_in equals 3
+   *
+   * This kernel is written exclusively for convolution with ch_im_in
+   * equals 3. This applies on the first layer of CNNs which has input
+   * image with RGB format.
+   */
+
+arm_status
+arm_convolve_HWC_q7_RGB(const q7_t * Im_in,
+                        const uint16_t dim_im_in,
+                        const uint16_t ch_im_in,
+                        const q7_t * wt,
+                        const uint16_t ch_im_out,
+                        const uint16_t dim_kernel,
+                        const uint16_t padding,
+                        const uint16_t stride,
+                        const q7_t * bias,
+                        const uint16_t bias_shift,
+                        const uint16_t out_shift,
+                        q7_t * Im_out, const uint16_t dim_im_out, q15_t * bufferA, q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    /*
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    // check if number of input channels is 3
+    if (ch_im_in != 3)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+    // This part implements the im2col function
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* Equivalent to arm_fill_q15(0, pBuffer, ch_im_in) with assumption: ch_im_in = 3 */
+                        *__SIMD32(pBuffer) = 0x0;
+                        *(pBuffer + 2) = 0;
+                        pBuffer += 3;
+                    } else
+                    {
+                        /* 
+                         * Equivalent to:
+                         *  arm_q7_to_q15_no_shift( (q7_t*)Im_in+(i_ker_y*dim_im_in+i_ker_x)*3, pBuffer, 3);
+                         */
+
+                        const q7_t *pPixel = Im_in + (i_ker_y * dim_im_in + i_ker_x) * 3;
+                        q31_t     buf = *__SIMD32(pPixel);
+
+                        union arm_nnword top;
+                        union arm_nnword bottom;
+
+                        top.word = __SXTB16(buf);
+                        bottom.word = __SXTB16(__ROR(buf, 8));
+
+#ifndef ARM_MATH_BIG_ENDIAN
+                        /*
+                         *  little-endian, | omit | 3rd  | 2nd  | 1st  |
+                         *                MSB                         LSB
+                         *   top | 3rd | 1st |; bottom | omit | 2nd |
+                         *
+                         *  version 1, need to swap 2nd and 3rd weight
+                         * *__SIMD32(pBuffer) = top.word;
+                         * *(pBuffer+2) = bottom.half_words[0];
+                         *
+                         *  version 2, no weight shuffling required
+                         */
+                        *pBuffer++ = top.half_words[0];
+                        *__SIMD32(pBuffer) = __PKHBT(bottom.word, top.word, 0);
+#else
+                        /*
+                         *  big-endian,    | 1st  | 2nd  | 3rd  | omit | 
+                         *                MSB                         LSB
+                         *  top | 2nd | omit |; bottom | 1st | 3rd |
+                         * 
+                         *  version 1, need to swap 2nd and 3rd weight
+                         * *__SIMD32(pBuffer) = bottom.word;
+                         * *(pBuffer+2) = top.half_words[1];
+                         * 
+                         *  version 2, no weight shuffling required
+                         */
+                        *pBuffer++ = bottom.half_words[0];
+                        *__SIMD32(pBuffer) = __PKHTB(top.word, bottom.word, 0);
+#endif
+                        pBuffer += 2;
+                    }
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * 3 * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15(wt, bufferA,
+                                                  ch_im_out,
+                                                  3 * dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* left-over because odd number of output pixels */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        int       i;
+
+        for (i = 0; i < ch_im_out; i++)
+        {
+            q31_t     sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+            q15_t    *pB = bufferA;
+            /* basically each time it process 4 entries */
+            uint16_t  colCnt = 3 * dim_kernel * dim_kernel >> 2;
+
+            while (colCnt)
+            {
+
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = 3 * dim_kernel * dim_kernel & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        }
+    }
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    // check if number of input channels is 3
+    if (ch_im_in != 3)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+                conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        /* if-for implementation */
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return (ARM_MATH_SUCCESS);
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic.c

@@ -0,0 +1,230 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q7_basic.c
+ * Description:	 Q7 version of convolution
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Basic Q7 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code> 
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * This basic version is designed to work for any input tensor and weight
+   * dimension. 
+   */
+
+arm_status
+arm_convolve_HWC_q7_basic(const q7_t * Im_in,
+                          const uint16_t dim_im_in,
+                          const uint16_t ch_im_in,
+                          const q7_t * wt,
+                          const uint16_t ch_im_out,
+                          const uint16_t dim_kernel,
+                          const uint16_t padding,
+                          const uint16_t stride,
+                          const q7_t * bias,
+                          const uint16_t bias_shift,
+                          const uint16_t out_shift,
+                          q7_t * Im_out, 
+                          const uint16_t dim_im_out, 
+                          q15_t * bufferA, 
+                          q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    /* 
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    /* This part implements the im2col function */
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* Filling 0 for out-of-bound paddings */
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        /* Copying the pixel data to column */
+                        arm_q7_to_q15_no_shift((q7_t *)
+                                               Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            /* Computation is filed for every 2 columns */
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15(wt, bufferA,
+                                                  ch_im_out,
+                                                  ch_im_in *
+                                                  dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* left-over because odd number of output pixels */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        int       i;
+
+        for (i = 0; i < ch_im_out; i++)
+        {
+            /* Load the accumulator with bias first */
+            q31_t     sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+
+            /* Point to the beging of the im2col buffer */
+            q15_t    *pB = bufferA;
+
+            /* Each time it process 4 entries */
+            uint16_t  colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
+
+            while (colCnt)
+            {
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        }
+    }
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+                conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        // if-for implementation
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_basic_nonsquare.c

@@ -0,0 +1,228 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q7_basic.c
+ * Description:	 Q7 version of convolution
+ *
+ * $Date:        13. July 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Basic Q7 convolution function (non-sqaure shape)
+   * @param[in]       Im_in        pointer to input tensor
+   * @param[in]       dim_im_in_x  input tensor dimention x
+   * @param[in]       dim_im_in_y  input tensor dimention y
+   * @param[in]       ch_im_in     number of input tensor channels
+   * @param[in]       wt           pointer to kernel weights
+   * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel_x filter kernel size x
+   * @param[in]       dim_kernel_y filter kernel size y
+   * @param[in]       padding_x    padding size x
+   * @param[in]       padding_y    padding size y
+   * @param[in]       stride_x     convolution stride x
+   * @param[in]       stride_y     convolution stride y
+   * @param[in]       bias         pointer to bias
+   * @param[in]       bias_shift   amount of left-shift for bias
+   * @param[in]       out_shift    amount of right-shift for output
+   * @param[in,out]   Im_out       pointer to output tensor
+   * @param[in]       dim_im_out_x output tensor dimension x
+   * @param[in]       dim_im_out_y output tensor dimension y
+   * @param[in,out]   bufferA      pointer to buffer space for input
+   * @param[in,out]   bufferB      pointer to buffer space for output
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   */
+
+arm_status arm_convolve_HWC_q7_basic_nonsquare(const q7_t * Im_in,
+                                               const uint16_t dim_im_in_x,
+                                               const uint16_t dim_im_in_y,
+                                               const uint16_t ch_im_in,
+                                               const q7_t * wt,
+                                               const uint16_t ch_im_out,
+                                               const uint16_t dim_kernel_x,
+                                               const uint16_t dim_kernel_y,
+                                               const uint16_t padding_x,
+                                               const uint16_t padding_y,
+                                               const uint16_t stride_x,
+                                               const uint16_t stride_y,
+                                               const q7_t * bias,
+                                               const uint16_t bias_shift,
+                                               const uint16_t out_shift,
+                                               q7_t * Im_out,
+                                               const uint16_t dim_im_out_x,
+                                               const uint16_t dim_im_out_y,
+                                               q15_t * bufferA,
+                                               q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    /* 
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    /* This part implements the im2col function */
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* Filling 0 for out-of-bound paddings */
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        /* Copying the pixel data to column */
+                        arm_q7_to_q15_no_shift((q7_t *)
+                                               Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            /* Computation is filed for every 2 columns */
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_y * dim_kernel_x)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15(wt, bufferA,
+                                                  ch_im_out,
+                                                  ch_im_in *
+                                                  dim_kernel_y * dim_kernel_x, bias_shift, out_shift, bias, pOut);
+
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* left-over because odd number of output pixels */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        int       i;
+
+        for (i = 0; i < ch_im_out; i++)
+        {
+            /* Load the accumulator with bias first */
+            q31_t     sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+
+            /* Point to the beging of the im2col buffer */
+            q15_t    *pB = bufferA;
+
+            /* Each time it process 4 entries */
+            uint16_t  colCnt = ch_im_in * dim_kernel_y * dim_kernel_x >> 2;
+
+            while (colCnt)
+            {
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (q7_t *) read_and_pad((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = ch_im_in * dim_kernel_y * dim_kernel_x & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        }
+    }
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out_y; j++)
+        {
+            for (k = 0; k < dim_im_out_x; k++)
+            {
+                conv_out = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel_y; m++)
+                {
+                    for (n = 0; n < dim_kernel_x; n++)
+                    {
+                        // if-for implementation
+                        in_row = stride_y * j + m - padding_y;
+                        in_col = stride_x * k + n - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] * 
+                                         wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + 
+                                         (m * dim_kernel_x + n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast.c

@@ -0,0 +1,408 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q7_fast.c
+ * Description:  Fast Q7 version of convolution
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+  /**
+   * @brief Fast Q7 convolution function
+   * @param[in]       Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       wt          pointer to kernel weights
+   * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       bias        pointer to bias
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input 
+   * @param[in,out]   bufferB     pointer to buffer space for output
+   * @return     The function returns either
+   * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+   *
+   * bufferB size: 0
+   *
+   * <b>Input dimension constraints:</b>
+   *
+   * ch_im_in is multiple of 4    ( because of the SIMD32 read and swap )
+   *
+   * ch_im_out is multipe of 2    ( bacause 2x2 mat_mult kernel )
+   *
+   * The im2col converts the Q7 tensor input into Q15 column, which is stored in
+   * bufferA. There is reordering happenning during this im2col process with
+   * arm_q7_to_q15_reordered_no_shift. For every four elements, the second and
+   * third elements are swapped. 
+   *
+   * The computation kernel arm_nn_mat_mult_kernel_q7_q15_reordered does the
+   * GEMM computation with the reordered columns.
+   *
+   * To speed-up the determination of the padding condition, we split the
+   * computation into 3x3 parts, i.e., {top, mid, bottom} X {left, mid, right}.
+   * This reduces the total number of boundary condition checks and improves
+   * the data copying performance.
+   */
+
+arm_status
+arm_convolve_HWC_q7_fast(const q7_t * Im_in,
+                         const uint16_t dim_im_in,
+                         const uint16_t ch_im_in,
+                         const q7_t * wt,
+                         const uint16_t ch_im_out,
+                         const uint16_t dim_kernel,
+                         const uint16_t padding,
+                         const uint16_t stride,
+                         const q7_t * bias,
+                         const uint16_t bias_shift,
+                         const uint16_t out_shift,
+                         q7_t * Im_out, 
+                         const uint16_t dim_im_out, 
+                         q15_t * bufferA, 
+                         q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    /*
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    /*
+     *  Here we split the entire matrix into three regions depending on the padding situation
+     *    Top: i_out_y from 0 to padding - 1
+     * Middle: i_out_y from padding to dim_im_out-padding-1
+     * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1
+     */
+
+    /* top part */
+    for (i_out_y = 0; i_out_y < padding; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift
+                            ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+                                                            bufferA,
+                                                            ch_im_out,
+                                                            ch_im_in
+                                                            *
+                                                            dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* middle part, here we also divide the x into left, mid and right */
+    for (; i_out_y < dim_im_out - padding; i_out_y++)
+    {
+
+        /* left part */
+        for (i_out_x = 0; i_out_x < padding; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift
+                            ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+                                                            bufferA,
+                                                            ch_im_out,
+                                                            ch_im_in
+                                                            *
+                                                            dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+
+        /* mid part */
+        for (; i_out_x < dim_im_out - padding; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in
+                                                 +
+                                                 (i_ker_y *
+                                                  dim_im_in +
+                                                  i_out_x *
+                                                  stride - padding) * ch_im_in, pBuffer, ch_im_in * dim_kernel);
+                pBuffer += ch_im_in * dim_kernel;
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+                                                            bufferA,
+                                                            ch_im_out,
+                                                            ch_im_in
+                                                            *
+                                                            dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+
+        /* right part */
+        for (; i_out_x < dim_im_out; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift
+                            ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+                                                            bufferA,
+                                                            ch_im_out,
+                                                            ch_im_in
+                                                            *
+                                                            dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    for (; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift
+                            ((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel * dim_kernel)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt,
+                                                            bufferA,
+                                                            ch_im_out,
+                                                            ch_im_in
+                                                            *
+                                                            dim_kernel * dim_kernel, bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* check if there is left-over for compute */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        int       i;
+
+        for (i = 0; i < ch_im_out; i++)
+        {
+            q31_t     sum = ((q31_t)bias[i] << bias_shift) + NN_ROUND(out_shift);
+            q15_t    *pB = bufferA;
+            /* each time it process 4 entries */
+            uint16_t  colCnt = ch_im_in * dim_kernel * dim_kernel >> 2;
+
+            while (colCnt)
+            {
+
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (q7_t *) read_and_pad_reordered((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = ch_im_in * dim_kernel * dim_kernel & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut = (q7_t) __SSAT((sum >> out_shift), 8);
+            pOut++;
+
+        }
+
+    }
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    uint16_t  i, j, k, l, m, n;
+    int       conv_out;
+    signed char in_row, in_col;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out; j++)
+        {
+            for (k = 0; k < dim_im_out; k++)
+            {
+                conv_out = (bias[i] << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel; m++)
+                {
+                    for (n = 0; n < dim_kernel; n++)
+                    {
+                        // if-for implementation
+                        in_row = stride * j + m - padding;
+                        in_col = stride * k + n - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out +=
+                                    Im_in[(in_row * dim_im_in + in_col) * ch_im_in +
+                                          l] * wt[i * ch_im_in * dim_kernel * dim_kernel + (m * dim_kernel +
+                                                                                            n) * ch_im_in + l];
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_HWC_q7_fast_nonsquare.c

@@ -0,0 +1,379 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_convolve_HWC_q7_fast_nonsquare.c
+ * Description:  Fast Q7 version of convolution (non-sqaure shape)
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Fast Q7 convolution function (non-sqaure shape)
+ * @param[in]       Im_in        pointer to input tensor
+ * @param[in]       dim_im_in_x  input tensor dimention x
+ * @param[in]       dim_im_in_y  input tensor dimention y
+ * @param[in]       ch_im_in     number of input tensor channels
+ * @param[in]       wt           pointer to kernel weights
+ * @param[in]       ch_im_out    number of filters, i.e., output tensor channels
+ * @param[in]       dim_kernel_x filter kernel size x
+ * @param[in]       dim_kernel_y filter kernel size y
+ * @param[in]       padding_x    padding size x
+ * @param[in]       padding_y    padding size y
+ * @param[in]       stride_x     convolution stride x
+ * @param[in]       stride_y     convolution stride y
+ * @param[in]       bias         pointer to bias
+ * @param[in]       bias_shift   amount of left-shift for bias
+ * @param[in]       out_shift    amount of right-shift for output
+ * @param[in,out]   Im_out       pointer to output tensor
+ * @param[in]       dim_im_out_x output tensor dimension x
+ * @param[in]       dim_im_out_y output tensor dimension y
+ * @param[in,out]   bufferA      pointer to buffer space for input 
+ * @param[in,out]   bufferB      pointer to buffer space for output
+ * @return     The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ *   ch_im_in is multiple of 4
+ *   ch_im_out is multiple of 2
+ */
+
+arm_status arm_convolve_HWC_q7_fast_nonsquare(const q7_t * Im_in,
+                                              const uint16_t dim_im_in_x,
+                                              const uint16_t dim_im_in_y,
+                                              const uint16_t ch_im_in,
+                                              const q7_t * wt,
+                                              const uint16_t ch_im_out,
+                                              const uint16_t dim_kernel_x,
+                                              const uint16_t dim_kernel_y,
+                                              const uint16_t padding_x,
+                                              const uint16_t padding_y,
+                                              const uint16_t stride_x,
+                                              const uint16_t stride_y,
+                                              const q7_t * bias,
+                                              const uint16_t bias_shift,
+                                              const uint16_t out_shift,
+                                              q7_t * Im_out,
+                                              const uint16_t dim_im_out_x,
+                                              const uint16_t dim_im_out_y, 
+                                              q15_t * bufferA, 
+                                              q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x, i_ker_y, i_ker_x;
+
+    /* -----------------------
+     *  Here we use bufferA as q15_t internally as computation are done with q15_t level
+     *  im2col are done to output in q15_t format from q7_t input
+     */
+
+    q15_t    *pBuffer = bufferA;
+    q7_t     *pOut = Im_out;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    /*
+     *  Here we split the entire matrix into three regions depending on the padding situation
+     *    Top: i_out_y from 0 to padding - 1
+     * Middle: i_out_y from padding to dim_im_out-padding-1
+     * Bottom: i_out_y from dim_im_out-padding to dim_im_out-1
+     */
+
+    /* top part */
+    for (i_out_y = 0; i_out_y < padding_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+                     i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+                                                         pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+                                                  bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* middle part, here we also divide the x into left, mid and right */
+    for (; i_out_y < dim_im_out_y - padding_y; i_out_y++)
+    {
+
+        /* left part */
+        for (i_out_x = 0; i_out_x < padding_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+                     i_ker_x++)
+                {
+                    if (i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+                                                         pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+                                                  bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+
+        /* mid part */
+        for (; i_out_x < dim_im_out_x - padding_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in +
+                                                 (i_ker_y * dim_im_in_x + i_out_x * stride_x - padding_x) * ch_im_in,
+                                                 pBuffer, ch_im_in * dim_kernel_x);
+                pBuffer += ch_im_in * dim_kernel_x;
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+                                                  bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+
+        /* right part */
+        for (; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+                     i_ker_x++)
+                {
+                    if (i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+                                                         pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+                                                  bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    for (; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            /* This part implements the im2col function */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+                     i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q15(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, sizeof(q15_t)*ch_im_in);
+                    } else
+                    {
+                        arm_q7_to_q15_reordered_no_shift((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in,
+                                                         pBuffer, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            if (pBuffer == bufferA + 2 * ch_im_in * dim_kernel_x * dim_kernel_y)
+            {
+                pOut =
+                    arm_nn_mat_mult_kernel_q7_q15_reordered(wt, bufferA, ch_im_out, ch_im_in * dim_kernel_x * dim_kernel_y,
+                                                  bias_shift, out_shift, bias, pOut);
+                /* counter reset */
+                pBuffer = bufferA;
+            }
+        }
+    }
+
+    /* check if there is left-over for compute */
+    if (pBuffer != bufferA)
+    {
+        const q7_t *pA = wt;
+        int       i;
+        for (i = 0; i < ch_im_out; i++)
+        {
+            q31_t     sum = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+            q15_t    *pB = bufferA;
+            /* basically each time it process 4 entries */
+            uint16_t  colCnt = ch_im_in * dim_kernel_x * dim_kernel_y >> 2;
+
+            while (colCnt)
+            {
+
+                q31_t     inA1, inA2;
+                q31_t     inB1, inB2;
+
+                pA = (const q7_t *)read_and_pad_reordered((void *)pA, &inA1, &inA2);
+
+                inB1 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA1, inB1, sum);
+                inB2 = *__SIMD32(pB)++;
+                sum = __SMLAD(inA2, inB2, sum);
+
+                colCnt--;
+            }
+            colCnt = (ch_im_in * dim_kernel_y * dim_kernel_x) & 0x3;
+            while (colCnt)
+            {
+                q7_t      inA1 = *pA++;
+                q15_t     inB1 = *pB++;
+                sum += inA1 * inB1;
+                colCnt--;
+            }
+            *pOut = (q7_t) __SSAT((sum >> out_shift), 8);
+            pOut++;
+
+        }
+
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    int       i, j, k, l, m, n;
+    int       conv_out;
+    int       in_row, in_col;
+
+    if (ch_im_in % 4 != 0 || ch_im_out % 2 != 0)
+    {
+        /* check if the input dimension meets the constraints */
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i = 0; i < ch_im_out; i++)
+    {
+        for (j = 0; j < dim_im_out_y; j++)
+        {
+            for (k = 0; k < dim_im_out_x; k++)
+            {
+                conv_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+                for (m = 0; m < dim_kernel_y; m++)
+                {
+                    for (n = 0; n < dim_kernel_x; n++)
+                    {
+                        /* if-for implementation */
+                        in_row = stride_y * j + m - padding_y;
+                        in_col = stride_x * k + n - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            for (l = 0; l < ch_im_in; l++)
+                            {
+                                conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + l] *
+                                    wt[i * ch_im_in * dim_kernel_y * dim_kernel_x + (m * dim_kernel_x + n) * ch_im_in + l];      
+                            }
+                        }
+                    }
+                }
+                Im_out[i + (j * dim_im_out_x + k) * ch_im_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_u8_basic_ver1.c

@@ -0,0 +1,239 @@
+/*
+ * Copyright (C) 2010-2019 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_depthwise_conv_u8_basic_ver1.c
+ * Description:  u8 depthwise convolution function
+ *
+ * $Date:        June, 2019
+ * $Revision:    V.0.8.0
+ *
+ * Target :  Cortex-M cores with DSP extension
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+#include <stdint.h>
+#include <stdio.h>
+
+#define DILATION_X (1)
+#define DILATION_Y (1)
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief uint8 depthwise convolution function with asymmetric quantization for even number of channel multiplier
+ *        and input channels. Unless specified otherwise, arguments are mandatory. Both square and non-square inputs
+ *        are accepted.
+ *
+ * @param[in]     input     Pointer to input tensor
+ * @param[in]     input_x   Width of input tensor
+ * @param[in]     input_y   Height of input tensor
+ * @param[in]     input_ch  Channels in input tensor
+ * @param[in]     kernel    Pointer to kernel weights
+ * @param[in]     kernel_x  Width of kernel
+ * @param[in]     kernel_y  Height of kernel
+ * @param[in]     ch_mult   Number of channel multiplier
+ * @param[in]     pad_x     Padding sizes x
+ * @param[in]     pad_y     Padding sizes y
+ * @param[in]     stride_x  Convolution stride along the width
+ * @param[in]     stride_y  Convolution stride along the height
+ * @param[in]     dilation_x Dilation along width. Not used and intended for future enhancement.
+ * @param[in]     dilation_y Dilation along height. Not used and intended for future enhancement.
+ * @param[in]     bias       Pointer to optional bias values. If no bias is
+ *                           availble, NULL is expected
+ * @param[in]     input_offset  Input tensor zero offset
+ * @param[in]     filter_offset Kernel tensor zero offset
+ * @param[in]     output_offset Output tensor zero offset
+ * @param[in,out] output        Pointer to output tensor
+ * @param[in]     output_x  Width of output tensor
+ * @param[in]     output_y  Height of output tensor
+ * @param[in]     output_activation_min   Minimum value to clamp the output to. Range : {0, 255}
+ * @param[in]     output_activation_max   Minimum value to clamp the output to. Range : {0, 255}
+ * @param[in]     out_shift  Amount of right-shift for output
+ * @param[in]     out_mult   Output multiplier for requantization
+ * @return        The function returns one of the following
+ *                <code>ARM_MATH_SIZE_MISMATCH</code> - Not supported dimension of tensors
+ *                <code>ARM_MATH_SUCCESS</code> - Successful operation
+ *                <code>ARM_MATH_ARGUMENT_ERROR</code> - Implementation not available
+ *
+ * <b> Input constraints</b>
+ * ch_mult  is multiple of 2
+ * kernel_x is multiple of 2
+ *
+ */
+
+arm_status arm_depthwise_conv_u8_basic_ver1(const uint8_t *input,
+                                            const uint16_t input_x,
+                                            const uint16_t input_y,
+                                            const uint16_t input_ch,
+                                            const uint8_t *kernel,
+                                            const uint16_t kernel_x,
+                                            const uint16_t kernel_y,
+                                            const int16_t ch_mult,
+                                            const int16_t pad_x,
+                                            const int16_t pad_y,
+                                            const int16_t stride_x,
+                                            const int16_t stride_y,
+                                            const int16_t dilation_x,
+                                            const int16_t dilation_y,
+                                            const int32_t *bias,
+                                            const int32_t input_offset,
+                                            const int32_t filter_offset,
+                                            const int32_t output_offset,
+                                            uint8_t *output,
+                                            const uint16_t output_x,
+                                            const uint16_t output_y,
+                                            const int32_t output_activation_min,
+                                            const int32_t output_activation_max,
+                                            const int32_t out_shift,
+                                            const int32_t out_mult)
+{
+    arm_status status = ARM_MATH_SUCCESS;
+ #if defined (ARM_MATH_DSP)
+    int i_out = 0;
+    (void)dilation_x;
+    (void)dilation_y;
+
+    const int32_t input_offset_pkd = (input_offset & 0xFFFF) | (input_offset & 0xFFFF) << 16;
+    const int32_t kernel_offset_pkd = (filter_offset & 0xFFFF) | (filter_offset & 0xFFFF) << 16;
+
+    if (0 != ch_mult % 2 || 0 != kernel_x % 2)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (int i_out_y = 0; i_out_y < output_y; i_out_y++)
+    {
+        const int16_t base_idx_y = (i_out_y * stride_y) - pad_y;
+        for (int i_out_x = 0; i_out_x < output_x; i_out_x++)
+        {
+            const int16_t base_idx_x = (i_out_x * stride_x) - pad_x;
+            for (int i_input_ch = 0; i_input_ch < input_ch; i_input_ch++)
+            {
+                for (int i_ch_mult = 0; i_ch_mult < ch_mult; i_ch_mult += 2)
+                {
+                    const int idx_out_ch = i_ch_mult + i_input_ch * ch_mult;
+
+                    int32_t acc_0 = 0;
+                    int32_t acc_1 = 0;
+                    if (NULL != bias)
+                    {
+                        acc_0 = bias[idx_out_ch];
+                        acc_1 = bias[idx_out_ch + 1];
+                    }
+
+                    for (int i_ker_y = 0; i_ker_y < kernel_y; i_ker_y++)
+                    {
+                        const int32_t idx_y = base_idx_y + DILATION_Y * i_ker_y;
+                        const int32_t y_in_range = (idx_y >= 0) && (idx_y < input_y);
+
+                        for (int i_ker_x = 0; i_ker_x < kernel_x; i_ker_x += 2)
+                        {
+                            if (1 == y_in_range)
+                            {
+                                const int32_t idx_x = base_idx_x + DILATION_X * i_ker_x;
+                                const int32_t idx_x1 = base_idx_x + DILATION_X * (i_ker_x + 1);
+                                /* Range check for first input */
+                                if (idx_x >= 0 && idx_x < input_x)
+                                {
+                                    const int32_t idx_0 = (idx_y * input_x + idx_x) * input_ch + i_input_ch;
+
+                                    const int32_t ker_idx_0 =
+                                        (i_ker_y * kernel_x + i_ker_x) * (input_ch * ch_mult) + idx_out_ch;
+                                    const int32_t ker_idx_1 = ker_idx_0 + input_ch * ch_mult;
+
+                                    int32_t input_pkd = input[idx_0] | (input[idx_0 + input_ch] << 16);
+                                    int32_t kernel_pkd = kernel[ker_idx_0] | (kernel[ker_idx_1] << 16);
+
+                                    input_pkd = __SADD16(input_pkd, input_offset_pkd);
+                                    kernel_pkd = __SADD16(kernel_pkd, kernel_offset_pkd);
+                                    /* Range check for second input */
+                                    if (idx_x1 >= input_x)
+                                    {
+                                        input_pkd &= 0xFFFF;
+                                    }
+                                    acc_0 = __SMLAD(input_pkd, kernel_pkd, acc_0);
+
+                                    kernel_pkd = kernel[ker_idx_0 + 1] | (kernel[ker_idx_1 + 1] << 16);
+                                    kernel_pkd = __SADD16(kernel_pkd, kernel_offset_pkd);
+                                    acc_1 = __SMLAD(input_pkd, kernel_pkd, acc_1);
+                                }
+                            }
+                        }
+                    }
+
+                    /* Requantize and clamp output to provided range */
+                    acc_0 = arm_nn_divide_by_power_of_two(arm_nn_sat_doubling_high_mult(
+                                                              acc_0 * (1 << LEFT_SHIFT(out_shift)), out_mult),
+                                                          RIGHT_SHIFT(out_shift));
+
+                    acc_0 += output_offset;
+
+                    if (output_activation_min > acc_0)
+                    {
+                        acc_0 = output_activation_min;
+                    }
+
+                    if (acc_0 > output_activation_max)
+                    {
+                        acc_0 = output_activation_max;
+                    }
+                    output[i_out++] = acc_0;
+
+                    /* Requantize and clamp output to provided range */
+                    acc_1 = arm_nn_divide_by_power_of_two(arm_nn_sat_doubling_high_mult(
+                                                              acc_1 * (1 << LEFT_SHIFT(out_shift)), out_mult),
+                                                          RIGHT_SHIFT(out_shift));
+                    acc_1 += output_offset;
+
+                    if (output_activation_min > acc_1)
+                    {
+                        acc_1 = output_activation_min;
+                    }
+
+                    if (acc_1 > output_activation_max)
+                    {
+                        acc_1 = output_activation_max;
+                    }
+                    output[i_out++] = acc_1;
+                }
+            }
+        }
+    }
+#else
+    /* No available implementation. */
+    status = ARM_MATH_ARGUMENT_ERROR;
+#endif
+    return status;
+}
+
+/**
+ * @} end of NNConv group
+ */
+
+

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7.c

@@ -0,0 +1,418 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_depthwise_separable_conv_HWC_q7.c
+ * Description:  Q7 depthwise separable convolution function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Q7 depthwise separable convolution function
+ * @param[in]       Im_in       pointer to input tensor
+ * @param[in]       dim_im_in   input tensor dimention
+ * @param[in]       ch_im_in    number of input tensor channels
+ * @param[in]       wt          pointer to kernel weights
+ * @param[in]       ch_im_out   number of filters, i.e., output tensor channels
+ * @param[in]       dim_kernel  filter kernel size
+ * @param[in]       padding     padding sizes
+ * @param[in]       stride      convolution stride
+ * @param[in]       bias        pointer to bias
+ * @param[in]       bias_shift  amount of left-shift for bias
+ * @param[in]       out_shift   amount of right-shift for output
+ * @param[in,out]   Im_out      pointer to output tensor
+ * @param[in]       dim_im_out  output tensor dimension
+ * @param[in,out]   bufferA     pointer to buffer space for input
+ * @param[in,out]   bufferB     pointer to buffer space for output
+ * @return     The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * @details
+ *
+ * <b>Buffer size:</b>
+ *
+ * bufferA size: 2*ch_im_in*dim_kernel*dim_kernel
+ *
+ * bufferB size: 0
+ *
+ * <b>Input dimension constraints:</b>
+ *
+ * ch_im_in equals ch_im_out
+ *
+ * Implementation:
+ * There are 3 nested loop here:
+ * Inner loop: calculate each output value with MAC instruction over an accumulator
+ * Mid   loop: loop over different output channel
+ * Outer loop: loop over different output (x, y)
+ */
+
+arm_status arm_depthwise_separable_conv_HWC_q7(const q7_t * Im_in,
+                                               const uint16_t dim_im_in,
+                                               const uint16_t ch_im_in,
+                                               const q7_t * wt,
+                                               const uint16_t ch_im_out,
+                                               const uint16_t dim_kernel,
+                                               const uint16_t padding,
+                                               const uint16_t stride,
+                                               const q7_t * bias,
+                                               const uint16_t bias_shift,
+                                               const uint16_t out_shift,
+                                               q7_t * Im_out, 
+                                               const uint16_t dim_im_out, 
+                                               q15_t * bufferA, 
+                                               q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_out_y, i_out_x;
+    int16_t   i_ker_y, i_ker_x;
+    q7_t     *colBuffer = (q7_t *) bufferA;
+    q7_t     *pBuffer = colBuffer;
+    const q7_t *pBias = bias;
+    q7_t     *pOut = Im_out;
+    uint16_t  rowCnt;
+    uint16_t  row_shift;
+
+    /* do some checking here, basically ch_im_in == ch_im_out */
+    if (ch_im_in != ch_im_out)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            /* we first do im2col here */
+            for (i_ker_y = i_out_y * stride - padding; i_ker_y < i_out_y * stride - padding + dim_kernel; i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride - padding; i_ker_x < i_out_x * stride - padding + dim_kernel; i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in || i_ker_x < 0 || i_ker_x >= dim_im_in)
+                    {
+                        /* arm_fill_q7(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, ch_im_in);
+                    } else
+                    {
+                        /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+                        memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in + i_ker_x) * ch_im_in, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            /* we will do the computation here for each channel */
+            rowCnt = ch_im_out >> 2;
+            row_shift = 0;
+            pBias = bias;
+
+            while (rowCnt)
+            {
+                q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+                uint16_t  colCnt = (dim_kernel * dim_kernel) >> 1;
+                q7_t     *pB = colBuffer + row_shift;
+                const q7_t *pA = wt + row_shift;
+                row_shift += 4;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+                while (colCnt)
+                {
+                    q31_t     inA1, inA2, inB1, inB2, opA, opB;
+
+                    inB1 = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    opB = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    inB2 = __PKHTB(opB, inB1, 16);
+                    inB1 = __PKHBT(inB1, opB, 16);
+                    inA1 = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    opB = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inA2 = __PKHTB(opB, inA1, 16);
+                    inA1 = __PKHBT(inA1, opB, 16);
+                    opA = __SXTB16(inA1);
+                    opB = __SXTB16(inB1);
+                    sum = __SMLAD(opA, opB, sum);
+                    opA = __SXTB16(__ROR(inA1, 8));
+                    opB = __SXTB16(__ROR(inB1, 8));
+                    sum2 = __SMLAD(opA, opB, sum2);
+                    opA = __SXTB16(inA2);
+                    opB = __SXTB16(inB2);
+                    sum3 = __SMLAD(opA, opB, sum3);
+                    opA = __SXTB16(__ROR(inA2, 8));
+                    opB = __SXTB16(__ROR(inB2, 8));
+                    sum4 = __SMLAD(opA, opB, sum4);
+                    colCnt--;
+                }
+#else
+
+                while (colCnt)
+                {
+                    q31_t     inA1, inA2, inB1, inB2, opA, opB;
+
+                    inB1 = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    opB = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    inB2 = __PKHBT(opB, inB1, 16);
+                    inB1 = __PKHTB(inB1, opB, 16);
+                    inA1 = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    opB = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inA2 = __PKHBT(opB, inA1, 16);
+                    inA1 = __PKHTB(inA1, opB, 16);
+                    opA = __SXTB16(inA1);
+                    opB = __SXTB16(inB1);
+                    sum2 = __SMLAD(opA, opB, sum2);
+                    opA = __SXTB16(__ROR(inA1, 8));
+                    opB = __SXTB16(__ROR(inB1, 8));
+                    sum = __SMLAD(opA, opB, sum);
+                    opA = __SXTB16(inA2);
+                    opB = __SXTB16(inB2);
+                    sum4 = __SMLAD(opA, opB, sum4);
+                    opA = __SXTB16(__ROR(inA2, 8));
+                    opB = __SXTB16(__ROR(inB2, 8));
+                    sum3 = __SMLAD(opA, opB, sum3);
+                    colCnt--;
+                }
+
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+#ifndef ARM_MATH_BIG_ENDIAN
+                /*
+                 *   r0    r1    r2    r3    r4   r5
+                 *  inA1, inA2, inB1, inB2, opA, opB
+                 */
+
+                asm volatile ("COL_LOOP_%=:\n"
+                              "ldr.w r2, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "pkhtb r3, r5, r2, ASR #16\n"
+                              "pkhbt r2, r2, r5, LSL #16\n"
+                              "ldr.w r0, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "pkhtb r1, r5, r0, ASR #16\n"
+                              "pkhbt r0, r0, r5, LSL #16\n"
+                              "sxtb16 r4, r0\n"
+                              "sxtb16 r5, r2\n"
+                              "smlad %[sum], r4, r5, %[sum]\n"
+                              "mov.w r4, r0, ror #8\n"
+                              "mov.w r5, r2, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum2], r4, r5, %[sum2]\n"
+                              "sxtb16 r4, r1\n"
+                              "sxtb16 r5, r3\n"
+                              "smlad %[sum3], r4, r5, %[sum3]\n"
+                              "mov.w r4, r1, ror #8\n"
+                              "mov.w r5, r3, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum4], r4, r5, %[sum4]\n"
+                              "subs %[colCnt], #1\n"
+                              "bne COL_LOOP_%=\n":[sum]
+                              "+r"(sum),[sum2] "+r"(sum2),
+                              [sum3] "+r"(sum3),
+                              [sum4] "+r"(sum4),[pB] "+r"(pB),
+                              [pA] "+r"(pA):[colCnt]
+                              "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+#else
+                /*
+                 *  r0    r1    r2    r3    r4   r5
+                 * inA1, inA2, inB1, inB2, opA, opB
+                 */
+                asm volatile ("COL_LOOP_%=:\n"
+                              "ldr.w r2, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "pkhbt r3, r5, r2, LSL #16\n"
+                              "pkhtb r2, r2, r5, ASR #16\n"
+                              "ldr.w r0, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "pkhbt r1, r5, r0, LSL #16\n"
+                              "pkhtb r0, r0, r5, ASR #16\n"
+                              "sxtb16 r4, r0\n"
+                              "sxtb16 r5, r2\n"
+                              "smlad %[sum2], r4, r5, %[sum2]\n"
+                              "mov.w r4, r0, ror #8\n"
+                              "mov.w r5, r2, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum], r4, r5, %[sum]\n"
+                              "sxtb16 r4, r1\n"
+                              "sxtb16 r5, r3\n"
+                              "smlad %[sum4], r4, r5, %[sum4]\n"
+                              "mov.w r4, r1, ror #8\n"
+                              "mov.w r5, r3, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum3], r4, r5, %[sum3]\n"
+                              "subs %[colCnt], #1\n"
+                              "bne COL_LOOP_%=\n":[sum]
+                              "+r"(sum),[sum2] "+r"(sum2),
+                              [sum3] "+r"(sum3),
+                              [sum4] "+r"(sum4),[pB] "+r"(pB),
+                              [pA] "+r"(pA):[colCnt]
+                              "r"(colCnt),[ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#endif                          /* USE_INTRINSIC */
+
+                colCnt = (dim_kernel * dim_kernel) & 0x1;
+                while (colCnt)
+                {
+                    union arm_nnword inA, inB;
+                    inA.word = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inB.word = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    sum += inA.bytes[0] * inB.bytes[0];
+                    sum2 += inA.bytes[1] * inB.bytes[1];
+                    sum3 += inA.bytes[2] * inB.bytes[2];
+                    sum4 += inA.bytes[3] * inB.bytes[3];
+                    colCnt--;
+                }
+
+                *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+                rowCnt--;
+            }
+
+            rowCnt = ch_im_out & 0x3;
+            while (rowCnt)
+            {
+                q7_t     *pB = colBuffer + row_shift;
+                const q7_t *pA = wt + row_shift;
+                q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                uint16_t  colCnt = (dim_kernel * dim_kernel);
+
+                row_shift += 1;
+
+                while (colCnt)
+                {
+                    q7_t      A1 = *pA;
+                    q7_t      B1 = *pB;
+                    pA += ch_im_in;
+                    pB += ch_im_in;
+                    sum += A1 * B1;
+
+                    colCnt--;
+                }
+                *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+                rowCnt--;
+            }
+
+            /* clear counter and pointers */
+            pBuffer = colBuffer;
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    int       i_out_y, i_out_x, i_ch_out, i_ker_x, i_ker_y;
+    int       conv_out;
+
+    /* do some checking here, basically ch_im_in == ch_im_out */
+    if (ch_im_in != ch_im_out)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i_out_y = 0; i_out_y < dim_im_out; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out; i_out_x++)
+        {
+            for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+            {
+                // for each output
+                conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
+                for (i_ker_y = 0; i_ker_y < dim_kernel; i_ker_y++)
+                {
+                    for (i_ker_x = 0; i_ker_x < dim_kernel; i_ker_x++)
+                    {
+                        int       in_row = stride * i_out_y + i_ker_y - padding;
+                        int       in_col = stride * i_out_x + i_ker_x - padding;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in && in_col < dim_im_in)
+                        {
+                            conv_out +=
+                                Im_in[(in_row *
+                                       dim_im_in +
+                                       in_col) *
+                                      ch_im_in +
+                                      i_ch_out] * wt[(i_ker_y * dim_kernel + i_ker_x) * ch_im_out + i_ch_out];
+                        }
+                    }
+                }
+                Im_out[(i_out_y * dim_im_out +
+                        i_out_x) * ch_im_out + i_ch_out] = (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_separable_conv_HWC_q7_nonsquare.c

@@ -0,0 +1,411 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_depthwise_separable_conv_HWC_q7_nonsquare.c
+ * Description:  Q7 depthwise separable convolution function (non-square shape)
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup NNConv
+ * @{
+ */
+
+/**
+ * @brief Q7 depthwise separable convolution function (non-square shape)
+ * @param[in]       Im_in         pointer to input tensor
+ * @param[in]       dim_im_in_x   input tensor dimention x
+ * @param[in]       dim_im_in_y   input tensor dimention y
+ * @param[in]       ch_im_in      number of input tensor channels
+ * @param[in]       wt            pointer to kernel weights
+ * @param[in]       ch_im_out     number of filters, i.e., output tensor channels
+ * @param[in]       dim_kernel_x  filter kernel size x
+ * @param[in]       dim_kernel_y  filter kernel size y
+ * @param[in]       padding_x     padding sizes x
+ * @param[in]       padding_y     padding sizes y
+ * @param[in]       stride_x      convolution stride x
+ * @param[in]       stride_y      convolution stride y
+ * @param[in]       bias          pointer to bias
+ * @param[in]       bias_shift    amount of left-shift for bias
+ * @param[in]       out_shift     amount of right-shift for output
+ * @param[in,out]   Im_out        pointer to output tensor
+ * @param[in]       dim_im_out_x  output tensor dimension x
+ * @param[in]       dim_im_out_y  output tensor dimension y
+ * @param[in,out]   bufferA       pointer to buffer space for input 
+ * @param[in,out]   bufferB       pointer to buffer space for output
+ * @return     The function returns either
+ * <code>ARM_MATH_SIZE_MISMATCH</code> or <code>ARM_MATH_SUCCESS</code> based on the outcome of size checking.
+ *
+ * This function is the version with full list of optimization tricks, but with
+ * some contraints:
+ *   ch_im_in is multiple of 2
+ *   ch_im_out is multiple of 2
+ */
+
+arm_status arm_depthwise_separable_conv_HWC_q7_nonsquare(const q7_t * Im_in,
+                                                         const uint16_t dim_im_in_x,
+                                                         const uint16_t dim_im_in_y,
+                                                         const uint16_t ch_im_in,
+                                                         const q7_t * wt,
+                                                         const uint16_t ch_im_out,
+                                                         const uint16_t dim_kernel_x,
+                                                         const uint16_t dim_kernel_y,
+                                                         const uint16_t padding_x,
+                                                         const uint16_t padding_y,
+                                                         const uint16_t stride_x,
+                                                         const uint16_t stride_y,
+                                                         const q7_t * bias,
+                                                         const uint16_t bias_shift,
+                                                         const uint16_t out_shift,
+                                                         q7_t * Im_out,
+                                                         const uint16_t dim_im_out_x,
+                                                         const uint16_t dim_im_out_y, 
+                                                         q15_t * bufferA, 
+                                                         q7_t * bufferB)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+/*
+ * Implementation:
+ * There are 3 nested loop here:
+ * Inner loop: calculate each output value with MAC instruction over an accumulator
+ * Mid   loop: loop over different output channel
+ * Outer loop: loop over different output (x, y)
+ *
+ */
+
+    int16_t   i_out_y, i_out_x;
+    int16_t   i_ker_y, i_ker_x;
+    q7_t     *colBuffer = (q7_t *) bufferA;
+    q7_t     *pBuffer = colBuffer;
+    const q7_t *pBias = bias;
+    q7_t     *pOut = Im_out;
+    uint16_t  rowCnt;
+    uint16_t  row_shift;
+
+    /* do some checking here, basically ch_im_in == ch_im_out */
+    if (ch_im_in != ch_im_out)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            /* we first do im2col here */
+            for (i_ker_y = i_out_y * stride_y - padding_y; i_ker_y < i_out_y * stride_y - padding_y + dim_kernel_y;
+                 i_ker_y++)
+            {
+                for (i_ker_x = i_out_x * stride_x - padding_x; i_ker_x < i_out_x * stride_x - padding_x + dim_kernel_x;
+                     i_ker_x++)
+                {
+                    if (i_ker_y < 0 || i_ker_y >= dim_im_in_y || i_ker_x < 0 || i_ker_x >= dim_im_in_x)
+                    {
+                        /* arm_fill_q7(0, pBuffer, ch_im_in); */
+                        memset(pBuffer, 0, ch_im_in);
+                    } else
+                    {
+                        /* arm_copy_q7((q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, pBuffer, ch_im_in); */
+                        memcpy(pBuffer, (q7_t *) Im_in + (i_ker_y * dim_im_in_x + i_ker_x) * ch_im_in, ch_im_in);
+                    }
+                    pBuffer += ch_im_in;
+                }
+            }
+
+            /* we will do the computation here for each channel */
+            rowCnt = ch_im_out >> 2;
+            row_shift = 0;
+            pBias = bias;
+
+            while (rowCnt)
+            {
+                q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+                uint16_t  colCnt = (dim_kernel_x * dim_kernel_y) >> 1;
+                q7_t     *pB = colBuffer + row_shift;
+                const q7_t *pA = wt + row_shift;
+                row_shift += 4;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+                while (colCnt)
+                {
+                    q31_t     inA1, inA2, inB1, inB2, opA, opB;
+
+                    inB1 = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    opB = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    inB2 = __PKHTB(opB, inB1, 16);
+                    inB1 = __PKHBT(inB1, opB, 16);
+                    inA1 = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    opB = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inA2 = __PKHTB(opB, inA1, 16);
+                    inA1 = __PKHBT(inA1, opB, 16);
+                    opA = __SXTB16(inA1);
+                    opB = __SXTB16(inB1);
+                    sum = __SMLAD(opA, opB, sum);
+                    opA = __SXTB16(__ROR(inA1, 8));
+                    opB = __SXTB16(__ROR(inB1, 8));
+                    sum2 = __SMLAD(opA, opB, sum2);
+                    opA = __SXTB16(inA2);
+                    opB = __SXTB16(inB2);
+                    sum3 = __SMLAD(opA, opB, sum3);
+                    opA = __SXTB16(__ROR(inA2, 8));
+                    opB = __SXTB16(__ROR(inB2, 8));
+                    sum4 = __SMLAD(opA, opB, sum4);
+                    colCnt--;
+                }
+#else
+
+                while (colCnt)
+                {
+                    q31_t     inA1, inA2, inB1, inB2, opA, opB;
+
+                    inB1 = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    opB = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    inB2 = __PKHBT(opB, inB1, 16);
+                    inB1 = __PKHTB(inB1, opB, 16);
+                    inA1 = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    opB = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inA2 = __PKHBT(opB, inA1, 16);
+                    inA1 = __PKHTB(inA1, opB, 16);
+                    opA = __SXTB16(inA1);
+                    opB = __SXTB16(inB1);
+                    sum2 = __SMLAD(opA, opB, sum2);
+                    opA = __SXTB16(__ROR(inA1, 8));
+                    opB = __SXTB16(__ROR(inB1, 8));
+                    sum = __SMLAD(opA, opB, sum);
+                    opA = __SXTB16(inA2);
+                    opB = __SXTB16(inB2);
+                    sum4 = __SMLAD(opA, opB, sum4);
+                    opA = __SXTB16(__ROR(inA2, 8));
+                    opB = __SXTB16(__ROR(inB2, 8));
+                    sum3 = __SMLAD(opA, opB, sum3);
+                    colCnt--;
+                }
+
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+#ifndef ARM_MATH_BIG_ENDIAN
+                //  r0    r1    r2    r3    r4   r5
+                // inA1, inA2, inB1, inB2, opA, opB
+                asm volatile ("COL_LOOP:\n"
+                              "ldr.w r2, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "pkhtb r3, r5, r2, ASR #16\n"
+                              "pkhbt r2, r2, r5, LSL #16\n"
+                              "ldr.w r0, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "pkhtb r1, r5, r0, ASR #16\n"
+                              "pkhbt r0, r0, r5, LSL #16\n"
+                              "sxtb16 r4, r0\n"
+                              "sxtb16 r5, r2\n"
+                              "smlad %[sum], r4, r5, %[sum]\n"
+                              "mov.w r4, r0, ror #8\n"
+                              "mov.w r5, r2, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum2], r4, r5, %[sum2]\n"
+                              "sxtb16 r4, r1\n"
+                              "sxtb16 r5, r3\n"
+                              "smlad %[sum3], r4, r5, %[sum3]\n"
+                              "mov.w r4, r1, ror #8\n"
+                              "mov.w r5, r3, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum4], r4, r5, %[sum4]\n"
+                              "subs %[colCnt], #1\n"
+                              "bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                              [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt),
+                              [ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+#else
+                //  r0    r1    r2    r3    r4   r5
+                // inA1, inA2, inB1, inB2, opA, opB
+                asm volatile ("COL_LOOP:\n"
+                              "ldr.w r2, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pB], #0]\n"
+                              "add.w %[pB], %[pB], %[ch_im_in]\n"
+                              "pkhbt r3, r5, r2, LSL #16\n"
+                              "pkhtb r2, r2, r5, ASR #16\n"
+                              "ldr.w r0, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "ldr.w r5, [%[pA], #0]\n"
+                              "add.w %[pA], %[pA], %[ch_im_in]\n"
+                              "pkhbt r1, r5, r0, LSL #16\n"
+                              "pkhtb r0, r0, r5, ASR #16\n"
+                              "sxtb16 r4, r0\n"
+                              "sxtb16 r5, r2\n"
+                              "smlad %[sum2], r4, r5, %[sum2]\n"
+                              "mov.w r4, r0, ror #8\n"
+                              "mov.w r5, r2, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum], r4, r5, %[sum]\n"
+                              "sxtb16 r4, r1\n"
+                              "sxtb16 r5, r3\n"
+                              "smlad %[sum4], r4, r5, %[sum4]\n"
+                              "mov.w r4, r1, ror #8\n"
+                              "mov.w r5, r3, ror #8\n"
+                              "sxtb16 r4, r4\n"
+                              "sxtb16 r5, r5\n"
+                              "smlad %[sum3], r4, r5, %[sum3]\n"
+                              "subs %[colCnt], #1\n"
+                              "bne COL_LOOP\n":[sum] "+r"(sum),[sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                              [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt),
+                              [ch_im_in] "r"(ch_im_in):"r0", "r1", "r2", "r3", "r4", "r5");
+#endif                          /*ARM_MATH_BIG_ENDIAN */
+
+#endif                          /* USE_INTRINSIC */
+
+                colCnt = (dim_kernel_x * dim_kernel_y) & 0x1;
+                while (colCnt)
+                {
+                    union arm_nnword inA, inB;
+                    inA.word = *__SIMD32(pA);
+                    pA += ch_im_in;
+                    inB.word = *__SIMD32(pB);
+                    pB += ch_im_in;
+                    sum += inA.bytes[0] * inB.bytes[0];
+                    sum2 += inA.bytes[1] * inB.bytes[1];
+                    sum3 += inA.bytes[2] * inB.bytes[2];
+                    sum4 += inA.bytes[3] * inB.bytes[3];
+                    colCnt--;
+                }
+
+                *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+                *pOut++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+                rowCnt--;
+            }
+
+            rowCnt = ch_im_out & 0x3;
+            while (rowCnt)
+            {
+                q7_t     *pB = colBuffer + row_shift;
+                const q7_t *pA = wt + row_shift;
+                q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+                uint16_t  colCnt = (dim_kernel_x * dim_kernel_y);
+
+                row_shift += 1;
+
+                while (colCnt)
+                {
+                    q7_t      A1 = *pA;
+                    q7_t      B1 = *pB;
+                    pA += ch_im_in;
+                    pB += ch_im_in;
+                    sum += A1 * B1;
+
+                    colCnt--;
+                }
+                *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+                rowCnt--;
+            }
+
+            // clear counter and pointers
+            pBuffer = colBuffer;
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    int       i_out_y, i_out_x, i_ch_out;
+    int       i_ker_y, i_ker_x; 
+
+    /* do some checking here, basically ch_im_in == ch_im_out */
+    if (ch_im_in != ch_im_out)
+    {
+        return ARM_MATH_SIZE_MISMATCH;
+    }
+
+    for (i_out_y = 0; i_out_y < dim_im_out_y; i_out_y++)
+    {
+        for (i_out_x = 0; i_out_x < dim_im_out_x; i_out_x++)
+        {
+            for (i_ch_out = 0; i_ch_out < ch_im_out; i_ch_out++)
+            {
+                // for each output 
+                int       conv_out = ((q31_t)(bias[i_ch_out]) << bias_shift) + NN_ROUND(out_shift);
+                for (i_ker_y = 0; i_ker_y < dim_kernel_y; i_ker_y++)
+                {
+                    for (i_ker_x = 0; i_ker_x < dim_kernel_x; i_ker_x++)
+                    {
+                        int       in_row = stride_y * i_out_y + i_ker_y - padding_y;
+                        int       in_col = stride_x * i_out_x + i_ker_x - padding_x;
+                        if (in_row >= 0 && in_col >= 0 && in_row < dim_im_in_y && in_col < dim_im_in_x)
+                        {
+                            conv_out += Im_in[(in_row * dim_im_in_x + in_col) * ch_im_in + i_ch_out] *                        
+                                wt[(i_ker_y * dim_kernel_x + i_ker_x) * ch_im_out + i_ch_out];
+                        }
+                    }
+                }
+                Im_out[(i_out_y * dim_im_out_x + i_out_x) * ch_im_out + i_ch_out] =
+                    (q7_t) __SSAT((conv_out >> out_shift), 8);
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+
+    /* Return to application */
+    return ARM_MATH_SUCCESS;
+
+}
+
+/**
+ * @} end of NNConv group
+ */

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15.c

@@ -0,0 +1,187 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_mat_mult_kernel_q7_q15.c
+ * Description:  Matrix-multiplication function for convolution
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+  /**
+   * @brief Matrix-multiplication function for convolution
+   * @param[in]       pA          pointer to operand A
+   * @param[in]       pInBuffer   pointer to operand B, always conssists of 2 vectors
+   * @param[in]       ch_im_out   numRow of A
+   * @param[in]       numCol_A    numCol of A
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        the bias
+   * @param[in,out]   pOut        pointer to output
+   * @return     The function returns the incremented output pointer
+   *
+   * @details
+   *
+   * This function does the matrix multiplication with weight matrix
+   * and 2 columns from im2col. 
+   */
+
+q7_t     *arm_nn_mat_mult_kernel_q7_q15(const q7_t * pA,
+                                        const q15_t * pInBuffer,
+                                        const uint16_t ch_im_out,
+                                        const uint16_t numCol_A,
+                                        const uint16_t bias_shift,
+                                        const uint16_t out_shift, 
+                                        const q7_t * bias, 
+                                        q7_t * pOut)
+{
+#if defined (ARM_MATH_DSP)
+    /* set up the second output pointers */
+    q7_t     *pOut2 = pOut + ch_im_out;
+    const q7_t *pBias = bias;
+
+    uint16_t  rowCnt = ch_im_out >> 1;
+    /* this loop over rows in A */
+    while (rowCnt)
+    {
+        /* setup pointers for B */
+        const q15_t *pB = pInBuffer;
+        const q15_t *pB2 = pB + numCol_A;
+
+        /* align the second pointer for A */
+        const q7_t *pA2 = pA + numCol_A;
+
+        /* init the sum with bias */
+        q31_t     sum =  ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = numCol_A >> 2;
+        /* accumulate over the vector */
+        while (colCnt)
+        {
+            q31_t     inA11, inA12, inA21, inA22;
+            q31_t     inB1 = *__SIMD32(pB)++;
+            q31_t     inB2 = *__SIMD32(pB2)++;
+
+            pA = (q7_t *) read_and_pad((void *)pA, &inA11, &inA12);
+            pA2 = (q7_t *) read_and_pad((void *)pA2, &inA21, &inA22);
+
+            sum = __SMLAD(inA11, inB1, sum);
+            sum2 = __SMLAD(inA11, inB2, sum2);
+            sum3 = __SMLAD(inA21, inB1, sum3);
+            sum4 = __SMLAD(inA21, inB2, sum4);
+
+            inB1 = *__SIMD32(pB)++;
+            inB2 = *__SIMD32(pB2)++;
+
+            sum = __SMLAD(inA12, inB1, sum);
+            sum2 = __SMLAD(inA12, inB2, sum2);
+            sum3 = __SMLAD(inA22, inB1, sum3);
+            sum4 = __SMLAD(inA22, inB2, sum4);
+
+            colCnt--;
+        }                       /* while over colCnt */
+        colCnt = numCol_A & 0x3;
+        while (colCnt)
+        {
+            q7_t      inA1 = *pA++;
+            q15_t     inB1 = *pB++;
+            q7_t      inA2 = *pA2++;
+            q15_t     inB2 = *pB2++;
+
+            sum += inA1 * inB1;
+            sum2 += inA1 * inB2;
+            sum3 += inA2 * inB1;
+            sum4 += inA2 * inB2;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+        *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+        *pOut2++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+        /* skip the row computed with A2 */
+        pA += numCol_A;
+        rowCnt--;
+    }                           /* for over ch_im_out */
+
+    /* compute left-over row if any */
+    if (ch_im_out & 0x1)
+    {
+        /* setup pointers for B */
+        const q15_t *pB = pInBuffer;
+        const q15_t *pB2 = pB + numCol_A;
+
+        /* load the bias */
+        q31_t     sum = ((q31_t)(*pBias) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = numCol_A >> 2;
+        while (colCnt)
+        {
+            q31_t     inA11, inA12;
+            q31_t     inB1 = *__SIMD32(pB)++;
+            q31_t     inB2 = *__SIMD32(pB2)++;
+
+            pA = (q7_t *) read_and_pad((void *)pA, &inA11, &inA12);
+
+            sum = __SMLAD(inA11, inB1, sum);
+            sum2 = __SMLAD(inA11, inB2, sum2);
+
+            inB1 = *__SIMD32(pB)++;
+            inB2 = *__SIMD32(pB2)++;
+            sum = __SMLAD(inA12, inB1, sum);
+            sum2 = __SMLAD(inA12, inB2, sum2);
+
+            colCnt--;
+        }
+        colCnt = numCol_A & 0x3;
+        while (colCnt)
+        {
+            q7_t      inA1 = *pA++;
+            q15_t     inB1 = *pB++;
+            q15_t     inB2 = *pB2++;
+
+            sum += inA1 * inB1;
+            sum2 += inA1 * inB2;
+            colCnt--;
+        }
+
+        *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+    }
+
+    pOut += ch_im_out;
+
+    /* return the new output pointer with offset */
+    return pOut;
+#else
+    /* To be completed */
+    return NULL;
+#endif                          /* ARM_MATH_DSP */
+
+}

Drivers/CMSIS/NN/Source/ConvolutionFunctions/arm_nn_mat_mult_kernel_q7_q15_reordered.c

@@ -0,0 +1,138 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_mat_mult_kernel_q7_q15_reordered.c
+ * Description:  Matrix-multiplication function for convolution with reordered columns
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnfunctions.h"
+#include "arm_math.h"
+
+  /**
+   * @brief Matrix-multiplication function for convolution with reordered columns
+   * @param[in]       pA          pointer to operand A
+   * @param[in]       pInBuffer   pointer to operand B, always conssists of 2 vectors
+   * @param[in]       ch_im_out   numRow of A
+   * @param[in]       numCol_A    numCol of A
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        the bias
+   * @param[in,out]   pOut        pointer to output
+   * @return     The function returns the incremented output pointer
+   *
+   * @details
+   *
+   * This function assumes that data in pInBuffer are reordered
+   */
+
+q7_t     *arm_nn_mat_mult_kernel_q7_q15_reordered(const q7_t * pA,
+                                                  const q15_t * pInBuffer,
+                                                  const uint16_t ch_im_out,
+                                                  const uint16_t numCol_A,
+                                                  const uint16_t bias_shift,
+                                                  const uint16_t out_shift, 
+                                                  const q7_t * bias, 
+                                                  q7_t * pOut)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* set up the second output pointers */
+    q7_t     *pOut2 = pOut + ch_im_out;
+    int       i;
+
+    /* this loop over rows in A */
+    for (i = 0; i < ch_im_out; i += 2)
+    {
+        /* setup pointers for B */
+        const q15_t *pB = pInBuffer;
+        const q15_t *pB2 = pB + numCol_A;
+
+        /* align the second pointer for A */
+        const q7_t *pA2 = pA + numCol_A;
+
+        /* init the sum with bias */
+        q31_t     sum =  ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(bias[i + 1]) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(bias[i + 1]) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = numCol_A >> 2;
+        /* accumulate over the vector */
+        while (colCnt)
+        {
+            q31_t     inA11, inA12, inA21, inA22;
+            q31_t     inB1 = *__SIMD32(pB)++;
+            q31_t     inB2 = *__SIMD32(pB2)++;
+
+            pA = (q7_t *) read_and_pad_reordered((void *)pA, &inA11, &inA12);
+            pA2 = (q7_t *) read_and_pad_reordered((void *)pA2, &inA21, &inA22);
+
+            sum = __SMLAD(inA11, inB1, sum);
+            sum2 = __SMLAD(inA11, inB2, sum2);
+            sum3 = __SMLAD(inA21, inB1, sum3);
+            sum4 = __SMLAD(inA21, inB2, sum4);
+
+            inB1 = *__SIMD32(pB)++;
+            inB2 = *__SIMD32(pB2)++;
+
+            sum = __SMLAD(inA12, inB1, sum);
+            sum2 = __SMLAD(inA12, inB2, sum2);
+            sum3 = __SMLAD(inA22, inB1, sum3);
+            sum4 = __SMLAD(inA22, inB2, sum4);
+
+            colCnt--;
+        }                       /* while over colCnt */
+        colCnt = numCol_A & 0x3;
+        while (colCnt)
+        {
+            q7_t      inA1 = *pA++;
+            q15_t     inB1 = *pB++;
+            q7_t      inA2 = *pA2++;
+            q15_t     inB2 = *pB2++;
+
+            sum += inA1 * inB1;
+            sum2 += inA1 * inB2;
+            sum3 += inA2 * inB1;
+            sum4 += inA2 * inB2;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pOut++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        *pOut++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+        *pOut2++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+        *pOut2++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+        /* skip the row computed with A2 */
+        pA += numCol_A;
+    }                           /* for over ch_im_out */
+
+    pOut += ch_im_out;
+
+    /* return the new output pointer with offset */
+    return pOut;
+#else
+    /* To be completed */
+    return NULL;
+#endif                          /* ARM_MATH_DSP */
+}

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c

@@ -0,0 +1,199 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_mat_q7_vec_q15.c
+ * Description:  Mixed Q15-Q7 fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Mixed Q15-Q7 fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: 0
+   *
+   *  Q7_Q15 version of the fully connected layer
+   *
+   *  Weights are in q7_t and Activations are in q15_t
+   *
+   */
+
+arm_status
+arm_fully_connected_mat_q7_vec_q15(const q15_t * pV,
+                                   const q7_t * pM,
+                                   const uint16_t dim_vec,
+                                   const uint16_t num_of_rows,
+                                   const uint16_t bias_shift,
+                                   const uint16_t out_shift, 
+                                   const q7_t * bias, 
+                                   q15_t * pOut, 
+                                   q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q7_t *pB = pM;
+    const q7_t *pB2;
+    q15_t    *pO = pOut;
+    const q7_t *pBias = bias;
+    const q15_t *pA = pV;
+
+    uint16_t  rowCnt = num_of_rows >> 1;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+        pB2 = pB + dim_vec;
+
+        while (colCnt)
+        {
+            q31_t     inV, inM11, inM12, inM21, inM22;
+            pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);
+            pB2 = (q7_t *) read_and_pad((void *)pB2, &inM21, &inM22);
+
+            inV = *__SIMD32(pA)++;
+
+            sum = __SMLAD(inV, inM11, sum);
+            sum2 = __SMLAD(inV, inM21, sum2);
+
+            inV = *__SIMD32(pA)++;
+
+            sum = __SMLAD(inV, inM12, sum);
+            sum2 = __SMLAD(inV, inM22, sum2);
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            q7_t      inM2 = *pB2++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16));
+
+        /*adjust the pointers and counters */
+        pB += dim_vec;
+        rowCnt--;
+    }
+
+    /* left-over part of the rows */
+    rowCnt = num_of_rows & 0x1;
+
+    while (rowCnt)
+    {
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inV2, inM11, inM12;
+
+            pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);
+
+            inV1 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV1, inM11, sum);
+
+            inV2 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV2, inM12, sum);
+
+            colCnt--;
+        }
+
+        /* left-over of the vector */
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            sum += inV * inM;
+            colCnt--;
+        }
+
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+
+        rowCnt--;
+    }
+
+#else
+    int       i, j;
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    for (i = 0; i < num_of_rows; i++)
+    {
+        int       ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+        for (j = 0; j < dim_vec; j++)
+        {
+            ip_out += pV[j] * pM[i * dim_vec + j];
+        }
+        pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16);
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to ARM_MATH_SUCCESS */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15_opt.c

@@ -0,0 +1,403 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_mat_q7_vec_q15_opt.c
+ * Description:  Mixed Q15-Q7 opt fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Mixed Q15-Q7 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: 0
+   *
+   *  Q7_Q15 version of the fully connected layer
+   *
+   *  Weights are in q7_t and Activations are in q15_t
+   *
+   *  Limitation: x4 version requires weight reordering to work
+   *
+   *  Here we use only one pointer to read 4 rows in the weight
+   *  matrix. So if the original q7_t matrix looks like this:
+   *
+   *  | a11 | a12 | a13 | a14 | a15 | a16 | a17 |
+   *
+   *  | a21 | a22 | a23 | a24 | a25 | a26 | a27 |
+   *
+   *  | a31 | a32 | a33 | a34 | a35 | a36 | a37 |
+   *
+   *  | a41 | a42 | a43 | a44 | a45 | a46 | a47 |
+   *
+   *  | a51 | a52 | a53 | a54 | a55 | a56 | a57 |
+   *
+   *  | a61 | a62 | a63 | a64 | a65 | a66 | a67 |
+   *
+   *  We operates on multiple-of-4 rows, so the first four rows becomes
+   *
+   *  | a11 | a21 | a12 | a22 | a31 | a41 | a32 | a42 |
+   *
+   *  | a13 | a23 | a14 | a24 | a33 | a43 | a34 | a44 |
+   *
+   *  | a15 | a25 | a16 | a26 | a35 | a45 | a36 | a46 |
+   *
+   *  The column left over will be in-order.
+   *  which is:
+   *  | a17 | a27 | a37 | a47 |
+   *
+   *  For the left-over rows, we do 1x1 computation, so the data remains
+   *  as its original order. 
+   *
+   *  So the stored weight matrix looks like this:
+   *
+   *  | a11 | a21 | a12 | a22 | a31 | a41 |
+   *
+   *  | a32 | a42 | a13 | a23 | a14 | a24 |
+   *
+   *  | a33 | a43 | a34 | a44 | a15 | a25 |
+   *
+   *  | a16 | a26 | a35 | a45 | a36 | a46 |
+   *
+   *  | a17 | a27 | a37 | a47 | a51 | a52 |
+   *
+   *  | a53 | a54 | a55 | a56 | a57 | a61 |
+   *
+   *  | a62 | a63 | a64 | a65 | a66 | a67 |
+   *
+   */
+
+arm_status
+arm_fully_connected_mat_q7_vec_q15_opt(const q15_t * pV,
+                                       const q7_t * pM,
+                                       const uint16_t dim_vec,
+                                       const uint16_t num_of_rows,
+                                       const uint16_t bias_shift,
+                                       const uint16_t out_shift, const q7_t * bias, q15_t * pOut, q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q7_t *pB = pM;
+    q15_t    *pO = pOut;
+    const q7_t *pBias = bias;
+    const q15_t *pA = pV;
+
+    uint16_t  rowCnt = num_of_rows >> 2;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 1;
+
+        pA = pV;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+        while (colCnt)
+        {
+            q31_t     inM11, inM12, inM13, inM14;
+            q31_t     inV;
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM11, inV, sum);
+            sum2 = __SMLAD(inM12, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM13, inV, sum3);
+            sum4 = __SMLAD(inM14, inV, sum4);
+            colCnt--;
+        }
+
+#else
+
+        while (colCnt)
+        {
+            q31_t     inM11, inM12, inM13, inM14;
+            q31_t     inV;
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM12, inV, sum);
+            sum2 = __SMLAD(inM11, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM14, inV, sum3);
+            sum4 = __SMLAD(inM13, inV, sum4);
+            colCnt--;
+        }
+
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+        /*
+         * register needed:
+         * loop counter: colCnt
+         * accumulators: sum, sum2, sum3, sum4
+         * pointers: pB, pA
+         * weight data: inM11, inM12, inM13, inM14
+         * activation data: inV
+         */
+
+#ifndef ARM_MATH_BIG_ENDIAN
+        asm volatile ("COL_LOOP_%=:\n"
+                      "ldr.w r4, [%[pA]], #4\n"
+                      "ldr.w r1, [%[pB]], #8\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r1, %[sum]\n"
+                      "smlad %[sum2], r4, r0, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-4]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r3, %[sum3]\n"
+                      "smlad %[sum4], r4, r2, %[sum4]\n"
+                      "subs %[colCnt], #1\n"
+                      "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+                      [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                      [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#else
+        asm volatile ("COL_LOOP_%=:\n"
+                      "ldr.w r4, [%[pA]], #4\n"
+                      "ldr.w r1, [%[pB]], #8\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r0, %[sum]\n"
+                      "smlad %[sum2], r4, r1, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-4]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r2, %[sum3]\n"
+                      "smlad %[sum4], r4, r3, %[sum4]\n"
+                      "subs %[colCnt], #1\n"
+                      "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+                      [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                      [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#endif                          /* USE_INTRINSIC */
+
+        colCnt = dim_vec & 0x1;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            q7_t      inM2 = *pB++;
+            q7_t      inM3 = *pB++;
+            q7_t      inM4 = *pB++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            sum3 += inV * inM3;
+            sum4 += inV * inM4;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16));
+
+        /* adjust the pointers and counters */
+        rowCnt--;
+    }
+
+    /* left-over part of the rows */
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inV2, inM11, inM12;
+
+            pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);
+
+            inV1 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV1, inM11, sum);
+
+            inV2 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV2, inM12, sum);
+
+            colCnt--;
+        }
+
+        /* left-over of the vector */
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            sum += inV * inM;
+            colCnt--;
+        }
+
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+
+        rowCnt--;
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  rowCnt = num_of_rows >> 2;
+    const q7_t *pB = pM;
+    const q15_t *pA;
+    q15_t    *pO = pOut;
+    const q7_t *pBias = bias;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); 
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); 
+        uint16_t  colCnt = dim_vec >> 1;
+
+        pA = pV;
+
+        while (colCnt)
+        {
+            q15_t     inA1 = *pA++;
+            q15_t     inA2 = *pA++;
+
+            q7_t      inB1 = *pB++;
+            q7_t      inB3 = *pB++;
+            q7_t      inB2 = *pB++;
+            q7_t      inB4 = *pB++;
+
+            sum += inA1 * inB1 + inA2 * inB2;
+            sum2 += inA1 * inB3 + inA2 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum3 += inA1 * inB1 + inA2 * inB2;
+            sum4 += inA1 * inB3 + inA2 * inB4;
+
+            colCnt--;
+        }
+
+        colCnt = dim_vec & 0x1;
+        while (colCnt)
+        {
+            q15_t     inA = *pA++;
+            q7_t      inB = *pB++;
+            sum += inA * inB;
+            inB = *pB++;
+            sum2 += inA * inB;
+            inB = *pB++;
+            sum3 += inA * inB;
+            inB = *pB++;
+            sum4 += inA * inB;
+
+            colCnt--;
+        }
+        *pO++ = (q15_t) __SSAT((sum >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16);
+
+        rowCnt--;
+    }
+
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        int       ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        int       j;
+
+        pA = pV;
+        for (j = 0; j < dim_vec; j++)
+        {
+            q15_t     inA = *pA++;
+            q7_t      inB = *pB++;
+            ip_out += inA * inB;
+        }
+        *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16);
+
+        rowCnt--;
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to ARM_MATH_SUCCESS */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15.c

@@ -0,0 +1,193 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_q15.c
+ * Description:  Q15 basic fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Q15 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: 0
+   *
+   */
+
+arm_status
+arm_fully_connected_q15(const q15_t * pV,
+                        const q15_t * pM,
+                        const uint16_t dim_vec,
+                        const uint16_t num_of_rows,
+                        const uint16_t bias_shift,
+                        const uint16_t out_shift, 
+                        const q15_t * bias, 
+                        q15_t * pOut,
+                        q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q15_t *pB = pM;
+    const q15_t *pB2 = pB + dim_vec;
+    q15_t    *pO = pOut;
+    const q15_t    *pA;
+    const q15_t    *pBias = bias;
+    uint16_t rowCnt = num_of_rows >> 1;
+
+    /* this loop loops over different output */
+    while (rowCnt) {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+        pB2 = pB + dim_vec;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inM1, inM2;
+            inV1 = *__SIMD32(pA)++;
+            inM1 = *__SIMD32(pB)++;
+            sum = __SMLAD(inV1, inM1, sum);
+            inM2 = *__SIMD32(pB2)++;
+            sum2 = __SMLAD(inV1, inM2, sum2);
+
+            inV1 = *__SIMD32(pA)++;
+            inM1 = *__SIMD32(pB)++;
+            sum = __SMLAD(inV1, inM1, sum);
+            inM2 = *__SIMD32(pB2)++;
+            sum2 = __SMLAD(inV1, inM2, sum2);
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q15_t     inM = *pB++;
+            q15_t     inM2 = *pB2++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ =  (q15_t) (__SSAT((sum >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum2>> out_shift), 16));
+		
+        /* adjust the pointers and counters */
+        pB = pB + dim_vec;
+        rowCnt --;
+    }
+
+    rowCnt = num_of_rows & 0x1;
+
+    while (rowCnt) {
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+      
+        while (colCnt) {
+            q31_t     inV1, inM1;
+            inV1 = *__SIMD32(pA)++;
+            inM1 = *__SIMD32(pB)++;
+            sum = __SMLAD(inV1, inM1, sum);
+            
+            inV1 = *__SIMD32(pA)++;
+            inM1 = *__SIMD32(pB)++;
+            sum = __SMLAD(inV1, inM1, sum);
+				
+            colCnt--;
+	}
+			
+	/* left-over of the vector */
+	colCnt = dim_vec & 0x3;
+	while(colCnt) {
+            q15_t     inV = *pA++;
+            q15_t     inM = *pB++;
+
+            sum += inV * inM;
+
+            colCnt--;
+	}
+
+        *pO++ =  (q15_t) (__SSAT((sum >> out_shift), 16));
+			
+        rowCnt --;
+    }
+
+#else
+    int       i, j;
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    for (i = 0; i < num_of_rows; i++)
+    {
+        int       ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+        for (j = 0; j < dim_vec; j++)
+        {
+            ip_out += pV[j] * pM[i * dim_vec + j];
+        }
+        pOut[i] = (q15_t) __SSAT((ip_out >> out_shift), 16);
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to application */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q15_opt.c

@@ -0,0 +1,332 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_q15_opt.c
+ * Description:  Q15 opt fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Q15 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: 0
+   *
+   *  Here we use only one pointer to read 4 rows in the weight
+   *  matrix. So if the original matrix looks like this:
+   *
+   *  | a11 | a12 | a13 |
+   *
+   *  | a21 | a22 | a23 |
+   *
+   *  | a31 | a32 | a33 |
+   *
+   *  | a41 | a42 | a43 |
+   *
+   *  | a51 | a52 | a53 |
+   *
+   *  | a61 | a62 | a63 |
+   *
+   *  We operates on multiple-of-4 rows, so the first four rows becomes
+   *
+   *  | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 |
+   *
+   *  | a13 | a23 | a33 | a43 |
+   *
+   *  Remaining rows are kept the same original order.
+   *
+   *  So the stored weight matrix looks like this:
+   *
+   *
+   *  | a11 | a12 | a21 | a22 | a31 | a32 | a41 | a42 |
+   *
+   *  | a13 | a23 | a33 | a43 | a51 | a52 | a53 | a61 |
+   *
+   *  | a62 | a63 |
+   */
+
+arm_status
+arm_fully_connected_q15_opt(const q15_t * pV,
+                            const q15_t * pM,
+                            const uint16_t dim_vec,
+                            const uint16_t num_of_rows,
+                            const uint16_t bias_shift,
+                            const uint16_t out_shift, 
+                            const q15_t * bias, 
+                            q15_t * pOut, 
+                            q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q15_t *pB = pM;
+    q15_t    *pO = pOut;
+    const q15_t *pBias = bias;
+    const q15_t *pA = pV;
+
+    uint16_t  rowCnt = num_of_rows >> 2;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); 
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); 
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift); 
+
+        uint16_t  colCnt = dim_vec >> 1;
+
+        pA = pV;
+
+#ifdef USE_INTRINSIC
+
+        while (colCnt)
+        {
+            q31_t     inM11, inM12, inM13, inM14;
+            q31_t     inV;
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            sum = __SMLAD(inV, inM11, sum);
+            inM12 = *__SIMD32(pB)++;
+            sum2 = __SMLAD(inV, inM12, sum2);
+            inM13 = *__SIMD32(pB)++;
+            sum3 = __SMLAD(inV, inM13, sum3);
+            inM14 = *__SIMD32(pB)++;
+            sum4 = __SMLAD(inV, inM14, sum4);
+            colCnt--;
+        }
+
+#else
+
+        /*
+         * register needed:
+         * loop counter: colCnt
+         * accumulators: sum, sum2, sum3, sum4
+         * pointers: pB, pA
+         * weight data: inM11, inM12, inM13, inM14
+         * activation data: inV
+         */
+
+        asm volatile ("COL_LOOP_%=:\n"
+                      "ldr.w r4, [%[pA]], #4\n"
+                      "ldr.w r0, [%[pB]], #16\n"
+                      "smlad %[sum], r4, r0, %[sum]\n"
+                      "ldr.w r1, [%[pB] , #-12]\n"
+                      "smlad %[sum2], r4, r1, %[sum2]\n"
+                      "ldr.w r2, [%[pB] , #-8]\n"
+                      "smlad %[sum3], r4, r2, %[sum3]\n"
+                      "ldr.w r3, [%[pB] , #-4]\n"
+                      "smlad %[sum4], r4, r3, %[sum4]\n"
+                      "subs %[colCnt], #1\n"
+                      "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+                      [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                      [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+
+#endif                          /* USE_INTRINSIC */
+
+        colCnt = dim_vec & 0x1;
+        while (colCnt)
+        {
+
+            q15_t     inV = *pA++;
+            q15_t     inM = *pB++;
+            q15_t     inM2 = *pB++;
+            q15_t     inM3 = *pB++;
+            q15_t     inM4 = *pB++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            sum3 += inV * inM3;
+            sum4 += inV * inM4;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum2 >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum3 >> out_shift), 16));
+        *pO++ = (q15_t) (__SSAT((sum4 >> out_shift), 16));
+
+        /* adjust the pointers and counters */
+        rowCnt--;
+    }
+
+    /* left-over part of the rows */
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inV2, inM1, inM2;
+
+            inM1 = *__SIMD32(pB)++;
+            inV1 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV1, inM1, sum);
+
+            inM2 = *__SIMD32(pB)++;
+            inV2 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV2, inM2, sum);
+
+            colCnt--;
+        }
+
+        /* left-over of the vector */
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q15_t     inM = *pB++;
+            sum += inV * inM;
+            colCnt--;
+        }
+
+        *pO++ = (q15_t) (__SSAT((sum >> out_shift), 16));
+
+        rowCnt--;
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  rowCnt = num_of_rows >> 2;
+    const q15_t *pB = pM;
+    const q15_t *pA;
+    q15_t    *pO = pOut;
+    const q15_t *pBias = bias;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 1;
+
+        pA = pV;
+        while (colCnt)
+        {
+            q15_t     inA1 = *pA++;
+            q15_t     inA2 = *pA++;
+
+            q15_t     inB1 = *pB++;
+            q15_t     inB2 = *pB++;
+            sum += inA1 * inB1 + inA2 * inB2;
+
+            inB1 = *pB++;
+            inB2 = *pB++;
+            sum2 += inA1 * inB1 + inA2 * inB2;
+
+            inB1 = *pB++;
+            inB2 = *pB++;
+            sum3 += inA1 * inB1 + inA2 * inB2;
+
+            inB1 = *pB++;
+            inB2 = *pB++;
+            sum4 += inA1 * inB1 + inA2 * inB2;
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x1;
+        while (colCnt)
+        {
+            q15_t     inA = *pA++;
+            q15_t     inB = *pB++;
+            sum += inA * inB;
+            inB = *pB++;
+            sum2 += inA * inB;
+            inB = *pB++;
+            sum3 += inA * inB;
+            inB = *pB++;
+            sum4 += inA * inB;
+            colCnt--;
+        }
+        *pO++ = (q15_t) __SSAT((sum >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum2 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum3 >> out_shift), 16);
+        *pO++ = (q15_t) __SSAT((sum4 >> out_shift), 16);
+
+        rowCnt--;
+    }
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        int       ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        int       j;
+
+        pA = pV;
+        for (j = 0; j < dim_vec; j++)
+        {
+            q15_t     inA = *pA++;
+            q15_t     inB = *pB++;
+            ip_out += inA * inB;
+        }
+        *pO++ = (q15_t) __SSAT((ip_out >> out_shift), 16);
+
+        rowCnt--;
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to ARM_MATH_SUCCESS */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7.c

@@ -0,0 +1,198 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_q7.c
+ * Description:  Q7 basic fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Q7 basic fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: dim_vec
+   *
+   * This basic function is designed to work with regular weight
+   * matrix without interleaving.
+   *
+   */
+
+arm_status
+arm_fully_connected_q7(const q7_t * pV,
+                       const q7_t * pM,
+                       const uint16_t dim_vec,
+                       const uint16_t num_of_rows,
+                       const uint16_t bias_shift,
+                       const uint16_t out_shift, const q7_t * bias, q7_t * pOut, q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q7_t *pB = pM;
+    const q7_t *pB2;
+    q7_t     *pO = pOut;
+    const q7_t *pBias = bias;
+    q15_t    *pA;
+    uint16_t  rowCnt = num_of_rows >> 1;
+
+    /* expand the vector into the buffer */
+    arm_q7_to_q15_reordered_no_shift(pV, vec_buffer, dim_vec);
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = vec_buffer;
+        pB2 = pB + dim_vec;
+
+        while (colCnt)
+        {
+            q31_t     inV, inM11, inM12, inM21, inM22;
+            pB = (q7_t *) read_and_pad_reordered((void *)pB, &inM11, &inM12);
+            pB2 = (q7_t *) read_and_pad_reordered((void *)pB2, &inM21, &inM22);
+
+            inV = *__SIMD32(pA)++;
+
+            sum = __SMLAD(inV, inM11, sum);
+            sum2 = __SMLAD(inV, inM21, sum2);
+
+            inV = *__SIMD32(pA)++;
+
+            sum = __SMLAD(inV, inM12, sum);
+            sum2 = __SMLAD(inV, inM22, sum2);
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q7_t      inV = *pA++;
+            q15_t     inM = *pB++;
+            q15_t     inM2 = *pB2++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+        *pO++ = (q7_t) (__SSAT((sum2 >> out_shift), 8));
+
+        /* adjust the pointers and counters */
+        pB += dim_vec;
+        rowCnt--;
+    }
+
+    /* left-over part of the rows */
+    rowCnt = num_of_rows & 0x1;
+
+    while (rowCnt)
+    {
+        uint16_t  colCnt = dim_vec >> 2;
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        pA = vec_buffer;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inV2, inM11, inM12;
+
+            pB = (q7_t *) read_and_pad_reordered((void *)pB, &inM11, &inM12);
+
+            inV1 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV1, inM11, sum);
+
+            inV2 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV2, inM12, sum);
+
+            colCnt--;
+        }
+
+        /* left-over of the vector */
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q7_t      inV = *pA++;
+            q15_t     inM = *pB++;
+            sum += inV * inM;
+            colCnt--;
+        }
+
+        *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+
+        rowCnt--;
+    }
+
+#else
+    int       i, j;
+
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    for (i = 0; i < num_of_rows; i++)
+    {
+        int       ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
+        for (j = 0; j < dim_vec; j++)
+        {
+            ip_out += pV[j] * pM[i * dim_vec + j];
+        }
+        pOut[i] = (q7_t) __SSAT((ip_out >> out_shift), 8);
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to ARM_MATH_SUCCESS */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_q7_opt.c

@@ -0,0 +1,484 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_fully_connected_q7_opt.c
+ * Description:  Q7 basic fully-connected layer function
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup FC
+ * @{
+ */
+
+  /**
+   * @brief Q7 opt fully-connected layer function
+   * @param[in]       pV          pointer to input vector
+   * @param[in]       pM          pointer to matrix weights
+   * @param[in]       dim_vec     length of the vector
+   * @param[in]       num_of_rows number of rows in weight matrix
+   * @param[in]       bias_shift  amount of left-shift for bias
+   * @param[in]       out_shift   amount of right-shift for output
+   * @param[in]       bias        pointer to bias
+   * @param[in,out]   pOut        pointer to output vector
+   * @param[in,out]   vec_buffer  pointer to buffer space for input
+   * @return     The function returns <code>ARM_MATH_SUCCESS</code>
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * vec_buffer size: dim_vec
+   *
+   * This opt function is designed to work with interleaved weight
+   * matrix. The vector input is assumed in q7_t format, we call
+   *  arm_q7_to_q15_no_shift_shuffle function to expand into
+   *  q15_t format with certain weight re-ordering, refer to the function
+   *  comments for more details.
+   *  Here we use only one pointer to read 4 rows in the weight
+   *  matrix. So if the original q7_t matrix looks like this:
+   *
+   *  | a11 | a12 | a13 | a14 | a15 | a16 | a17 |
+   *
+   *  | a21 | a22 | a23 | a24 | a25 | a26 | a27 |
+   *
+   *  | a31 | a32 | a33 | a34 | a35 | a36 | a37 |
+   *
+   *  | a41 | a42 | a43 | a44 | a45 | a46 | a47 |
+   *
+   *  | a51 | a52 | a53 | a54 | a55 | a56 | a57 |
+   *
+   *  | a61 | a62 | a63 | a64 | a65 | a66 | a67 |
+   *
+   *
+   *  We operates on multiple-of-4 rows, so the first four rows becomes
+   *
+   *  | a11 | a21 | a13 | a23 | a31 | a41 | a33 | a43 |
+   *
+   *  | a12 | a22 | a14 | a24 | a32 | a42 | a34 | a44 |
+   *
+   *  | a15 | a25 | a35 | a45 | a16 | a26 | a36 | a46 |
+   *
+   *  So within the kernel, we first read the re-ordered vector in as:
+   *
+   *  | b1  | b3  | and | b2  | b4  |
+   *
+   *  the four q31_t weights will look like
+   *
+   *  | a11 | a13 |, | a21 | a23 |, | a31 | a33 |, | a41 | a43 |
+   *
+   *  | a12 | a14 |, | a22 | a24 |, | a32 | a34 |, | a42 | a44 |
+   *
+   *  The column left over will be in-order.
+   *  which is:
+   *
+   *  | a17 | a27 | a37 | a47 |
+   *
+   *  For the left-over rows, we do 1x1 computation, so the data remains
+   *  as its original order. 
+   *
+   *  So the stored weight matrix looks like this:
+   *
+   *  | a11 | a21 | a13 | a23 | a31 | a41 |
+   *
+   *  | a33 | a43 | a12 | a22 | a14 | a24 |
+   *
+   *  | a32 | a42 | a34 | a44 | a15 | a25 |
+   *
+   *  | a35 | a45 | a16 | a26 | a36 | a46 |
+   *
+   *  | a17 | a27 | a37 | a47 | a51 | a52 |
+   *
+   *  | a53 | a54 | a55 | a56 | a57 | a61 |
+   *
+   *  | a62 | a63 | a64 | a65 | a66 | a67 |
+   *
+   *
+   */
+
+arm_status
+arm_fully_connected_q7_opt(const q7_t * pV,
+                           const q7_t * pM,
+                           const uint16_t dim_vec,
+                           const uint16_t num_of_rows,
+                           const uint16_t bias_shift,
+                           const uint16_t out_shift, 
+                           const q7_t * bias, 
+                           q7_t * pOut, 
+                           q15_t * vec_buffer)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    const q7_t *pB = pM;
+    q7_t     *pO = pOut;
+    const q7_t *pBias = bias;
+    q15_t    *pA;
+    uint16_t  rowCnt = num_of_rows >> 2;
+
+    arm_q7_to_q15_reordered_no_shift(pV, vec_buffer, dim_vec);
+
+    while (rowCnt)
+    {
+
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = vec_buffer;
+
+#ifdef USE_INTRINSIC
+
+#ifndef ARM_MATH_BIG_ENDIAN
+        while (colCnt)
+        {
+            q31_t     inM11, inM12, inM13, inM14;
+            q31_t     inV;
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM11, inV, sum);
+            sum2 = __SMLAD(inM12, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM13, inV, sum3);
+            sum4 = __SMLAD(inM14, inV, sum4);
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM11, inV, sum);
+            sum2 = __SMLAD(inM12, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM13, inV, sum3);
+            sum4 = __SMLAD(inM14, inV, sum4);
+            colCnt--;
+        }
+#else
+        while (colCnt)
+        {
+            q31_t     inM11, inM12, inM13, inM14;
+            q31_t     inV;
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM12, inV, sum);
+            sum2 = __SMLAD(inM11, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM14, inV, sum3);
+            sum4 = __SMLAD(inM13, inV, sum4);
+
+            inV = *__SIMD32(pA)++;
+            inM11 = *__SIMD32(pB)++;
+            inM12 = __SXTB16(__ROR(inM11, 8));
+            inM11 = __SXTB16(inM11);
+            sum = __SMLAD(inM12, inV, sum);
+            sum2 = __SMLAD(inM11, inV, sum2);
+            inM13 = *__SIMD32(pB)++;
+            inM14 = __SXTB16(__ROR(inM13, 8));
+            inM13 = __SXTB16(inM13);
+            sum3 = __SMLAD(inM14, inV, sum3);
+            sum4 = __SMLAD(inM13, inV, sum4);
+            colCnt--;
+        }
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#else
+
+        /*
+         * register needed:
+         * loop counter: colCnt
+         * accumulators: sum, sum2, sum3, sum4
+         * pointers: pB, pA
+         * weight data: inM11, inM12, inM13, inM14
+         * activation data: inV
+         */
+
+#ifndef ARM_MATH_BIG_ENDIAN
+        asm volatile ("COL_LOOP_%=:\n"
+                      "ldr.w r4, [%[pA]], #8\n"
+                      "ldr.w r1, [%[pB]], #16\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r1, %[sum]\n"
+                      "smlad %[sum2], r4, r0, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-12]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r3, %[sum3]\n"
+                      "smlad %[sum4], r4, r2, %[sum4]\n"
+                      "ldr.w r4, [%[pA], #-4]\n"
+                      "ldr.w r1, [%[pB], #-8]\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r1, %[sum]\n"
+                      "smlad %[sum2], r4, r0, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-4]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r3, %[sum3]\n"
+                      "smlad %[sum4], r4, r2, %[sum4]\n"
+                      "subs %[colCnt], #1\n"
+                      "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+                      [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                      [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#else
+        asm volatile ("COL_LOOP_%=:\n"
+                      "ldr.w r4, [%[pA]], #8\n"
+                      "ldr.w r1, [%[pB]], #16\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r0, %[sum]\n"
+                      "smlad %[sum2], r4, r1, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-12]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r2, %[sum3]\n"
+                      "smlad %[sum4], r4, r3, %[sum4]\n"
+                      "ldr.w r4, [%[pA], #-4]\n"
+                      "ldr.w r1, [%[pB], #-8]\n"
+                      "mov.w r0, r1, ror #8\n"
+                      "sxtb16 r0, r0\n"
+                      "sxtb16 r1, r1\n"
+                      "smlad %[sum], r4, r0, %[sum]\n"
+                      "smlad %[sum2], r4, r1, %[sum2]\n"
+                      "ldr.w r3, [%[pB], #-4]\n"
+                      "mov.w r2, r3, ror #8\n"
+                      "sxtb16 r2, r2\n"
+                      "sxtb16 r3, r3\n"
+                      "smlad %[sum3], r4, r2, %[sum3]\n"
+                      "smlad %[sum4], r4, r3, %[sum4]\n"
+                      "subs %[colCnt], #1\n"
+                      "bne COL_LOOP_%=\n":[sum] "+r"(sum),
+                      [sum2] "+r"(sum2),[sum3] "+r"(sum3),
+                      [sum4] "+r"(sum4),[pB] "+r"(pB),[pA] "+r"(pA):[colCnt] "r"(colCnt):"r0", "r1", "r2", "r3", "r4");
+#endif                          /* ARM_MATH_BIG_ENDIAN */
+
+#endif                          /* USE_INTRINSIC */
+
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            q7_t      inM2 = *pB++;
+            q7_t      inM3 = *pB++;
+            q7_t      inM4 = *pB++;
+
+            sum += inV * inM;
+            sum2 += inV * inM2;
+            sum3 += inV * inM3;
+            sum4 += inV * inM4;
+            colCnt--;
+        }                       /* while over colCnt */
+        *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+        *pO++ = (q7_t) (__SSAT((sum2 >> out_shift), 8));
+        *pO++ = (q7_t) (__SSAT((sum3 >> out_shift), 8));
+        *pO++ = (q7_t) (__SSAT((sum4 >> out_shift), 8));
+
+        /* adjust the pointers and counters */
+        rowCnt--;
+    }
+
+    /* left-over part of the rows */
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        q31_t     sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = vec_buffer;
+
+        while (colCnt)
+        {
+            q31_t     inV1, inV2, inM11, inM12;
+
+            pB = (q7_t *) read_and_pad_reordered((void *)pB, &inM11, &inM12);
+
+            inV1 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV1, inM11, sum);
+
+            inV2 = *__SIMD32(pA)++;
+            sum = __SMLAD(inV2, inM12, sum);
+
+            colCnt--;
+        }
+
+        /* left-over of the vector */
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q15_t     inV = *pA++;
+            q7_t      inM = *pB++;
+            sum += inV * inM;
+            colCnt--;
+        }
+
+        *pO++ = (q7_t) (__SSAT((sum >> out_shift), 8));
+
+        rowCnt--;
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+    uint16_t  rowCnt = num_of_rows >> 2;
+    const q7_t *pB = pM;
+    const q7_t *pA;
+    q7_t     *pO = pOut;
+    const q7_t *pBias = bias;
+
+    while (rowCnt)
+    {
+        q31_t     sum =  ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum3 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+        q31_t     sum4 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        uint16_t  colCnt = dim_vec >> 2;
+
+        pA = pV;
+
+        while (colCnt)
+        {
+            q7_t      inA1 = *pA++;
+            q7_t      inA3 = *pA++;
+            q7_t      inA2 = *pA++;
+            q7_t      inA4 = *pA++;
+
+            q7_t      inB1 = *pB++;
+            q7_t      inB3 = *pB++;
+            q7_t      inB2 = *pB++;
+            q7_t      inB4 = *pB++;
+
+            sum += inA1 * inB1 + inA2 * inB2;
+            sum2 += inA1 * inB3 + inA2 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum3 += inA1 * inB1 + inA2 * inB2;
+            sum4 += inA1 * inB3 + inA2 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum += inA3 * inB1 + inA4 * inB2;
+            sum2 += inA3 * inB3 + inA4 * inB4;
+
+            inB1 = *pB++;
+            inB3 = *pB++;
+            inB2 = *pB++;
+            inB4 = *pB++;
+
+            sum3 += inA3 * inB1 + inA4 * inB2;
+            sum4 += inA3 * inB3 + inA4 * inB4;
+
+            colCnt--;
+        }
+        colCnt = dim_vec & 0x3;
+        while (colCnt)
+        {
+            q7_t      inA = *pA++;
+            q7_t      inB = *pB++;
+            sum += inA * inB;
+            inB = *pB++;
+            sum2 += inA * inB;
+            inB = *pB++;
+            sum3 += inA * inB;
+            inB = *pB++;
+            sum4 += inA * inB;
+
+            colCnt--;
+        }
+        *pO++ = (q7_t) __SSAT((sum >> out_shift), 8);
+        *pO++ = (q7_t) __SSAT((sum2 >> out_shift), 8);
+        *pO++ = (q7_t) __SSAT((sum3 >> out_shift), 8);
+        *pO++ = (q7_t) __SSAT((sum4 >> out_shift), 8);
+
+        rowCnt--;
+    }
+
+    rowCnt = num_of_rows & 0x3;
+
+    while (rowCnt)
+    {
+        int       ip_out = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
+
+        int       j;
+
+        pA = pV;
+        for (j = 0; j < dim_vec; j++)
+        {
+            q7_t      inA = *pA++;
+            q7_t      inB = *pB++;
+            ip_out += inA * inB;
+        }
+        *pO++ = (q7_t) __SSAT((ip_out >> out_shift), 8);
+
+        rowCnt--;
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+    /* Return to ARM_MATH_SUCCESS */
+    return (ARM_MATH_SUCCESS);
+
+}
+
+/**
+ * @} end of FC group
+ */

Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q15.c

@@ -0,0 +1,147 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_mult_q15.c
+ * Description:  Q15 vector multiplication with variable output shifts
+ *
+ * $Date:        13. July 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnfunctions.h"
+
+/**    
+ * @ingroup groupSupport    
+ */
+
+/**
+ * @addtogroup NNBasicMath
+ * @{
+ */
+
+
+/**
+ * @brief           Q7 vector multiplication with variable output shifts
+ * @param[in]       *pSrcA        pointer to the first input vector
+ * @param[in]       *pSrcB        pointer to the second input vector
+ * @param[out]      *pDst         pointer to the output vector
+ * @param[in]       out_shift     amount of right-shift for output
+ * @param[in]       blockSize     number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q15 range [0x8000 0x7FFF] will be saturated.
+ */
+
+void arm_nn_mult_q15(
+  q15_t * pSrcA,
+  q15_t * pSrcB,
+  q15_t * pDst,
+  const uint16_t out_shift,
+  uint32_t blockSize)
+{
+  uint32_t blkCnt;                               /* loop counters */
+
+#if defined (ARM_MATH_DSP)
+
+/* Run the below code for Cortex-M4 and Cortex-M3 */
+  q31_t inA1, inA2, inB1, inB2;                  /* temporary input variables */
+  q15_t out1, out2, out3, out4;                  /* temporary output variables */
+  q31_t mul1, mul2, mul3, mul4;                  /* temporary variables */
+
+  /* loop Unrolling */
+  blkCnt = blockSize >> 2U;
+
+  /* First part of the processing with loop unrolling.  Compute 4 outputs at a time.
+   ** a second loop below computes the remaining 1 to 3 samples. */
+  while (blkCnt > 0U)
+  {
+    /* read two samples at a time from sourceA */
+    inA1 = *__SIMD32(pSrcA)++;
+    /* read two samples at a time from sourceB */
+    inB1 = *__SIMD32(pSrcB)++;
+    /* read two samples at a time from sourceA */
+    inA2 = *__SIMD32(pSrcA)++;
+    /* read two samples at a time from sourceB */
+    inB2 = *__SIMD32(pSrcB)++;
+
+    /* multiply mul = sourceA * sourceB */
+    mul1 = (q31_t) ((q15_t) (inA1 >> 16) * (q15_t) (inB1 >> 16));
+    mul2 = (q31_t) ((q15_t) inA1 * (q15_t) inB1);
+    mul3 = (q31_t) ((q15_t) (inA2 >> 16) * (q15_t) (inB2 >> 16));
+    mul4 = (q31_t) ((q15_t) inA2 * (q15_t) inB2);
+
+    /* saturate result to 16 bit */
+    out1 = (q15_t) __SSAT((mul1 + NN_ROUND(out_shift)) >> out_shift, 16);
+    out2 = (q15_t) __SSAT((mul2 + NN_ROUND(out_shift)) >> out_shift, 16);
+    out3 = (q15_t) __SSAT((mul3 + NN_ROUND(out_shift)) >> out_shift, 16);
+    out4 = (q15_t) __SSAT((mul4 + NN_ROUND(out_shift)) >> out_shift, 16);
+
+    /* store the result */
+#ifndef ARM_MATH_BIG_ENDIAN
+
+    *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16);
+    *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16);
+
+#else
+
+    *__SIMD32(pDst)++ = __PKHBT(out2, out1, 16);
+    *__SIMD32(pDst)++ = __PKHBT(out4, out3, 16);
+
+#endif /* #ifndef ARM_MATH_BIG_ENDIAN */
+
+    /* Decrement the blockSize loop counter */
+    blkCnt--;
+  }
+
+  /* If the blockSize is not a multiple of 4, compute any remaining output samples here.
+   ** No loop unrolling is used. */
+  blkCnt = blockSize % 0x4U;
+
+#else
+
+  /* Run the below code for Cortex-M0 */
+
+  /* Initialize blkCnt with number of samples */
+  blkCnt = blockSize;
+
+#endif /* #if defined (ARM_MATH_DSP) */
+
+
+  while (blkCnt > 0U)
+  {
+    /* C = A * B */
+    /* Multiply the inputs and store the result in the destination buffer */
+    *pDst++ = (q15_t) __SSAT((((q31_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 16);
+
+    /* Decrement the blockSize loop counter */
+    blkCnt--;
+  }
+}
+
+/**
+ * @} end of NNBasicMath group
+ */
+

Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nn_mult_q7.c

@@ -0,0 +1,119 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nn_mult_q7.c
+ * Description:  Q7 vector multiplication with variable output shifts
+ *
+ * $Date:        13. July 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnfunctions.h"
+
+/**    
+ * @ingroup groupSupport    
+ */
+
+/**
+ * @addtogroup NNBasicMath
+ * @{
+ */
+
+/**
+ * @brief           Q7 vector multiplication with variable output shifts
+ * @param[in]       *pSrcA        pointer to the first input vector
+ * @param[in]       *pSrcB        pointer to the second input vector
+ * @param[out]      *pDst         pointer to the output vector
+ * @param[in]       out_shift     amount of right-shift for output
+ * @param[in]       blockSize     number of samples in each vector
+ * @return none.
+ *
+ * <b>Scaling and Overflow Behavior:</b>
+ * \par
+ * The function uses saturating arithmetic.
+ * Results outside of the allowable Q7 range [0x80 0x7F] will be saturated.
+ */
+
+void arm_nn_mult_q7(
+  q7_t * pSrcA,
+  q7_t * pSrcB,
+  q7_t * pDst,
+  const uint16_t out_shift,
+  uint32_t blockSize)
+{
+  uint32_t blkCnt;                               /* loop counters */
+
+#if defined (ARM_MATH_DSP)
+
+/* Run the below code for Cortex-M4 and Cortex-M3 */
+  q7_t out1, out2, out3, out4;                   /* Temporary variables to store the product */
+
+  /* loop Unrolling */
+  blkCnt = blockSize >> 2U;
+
+  /* First part of the processing with loop unrolling.  Compute 4 outputs at a time.
+   ** a second loop below computes the remaining 1 to 3 samples. */
+  while (blkCnt > 0U)
+  {
+    /* C = A * B */
+    /* Multiply the inputs and store the results in temporary variables */
+    out1 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+    out2 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+    out3 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+    out4 = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+
+    /* Store the results of 4 inputs in the destination buffer in single cycle by packing */
+    *__SIMD32(pDst)++ = __PACKq7(out1, out2, out3, out4);
+
+    /* Decrement the blockSize loop counter */
+    blkCnt--;
+  }
+
+  /* If the blockSize is not a multiple of 4, compute any remaining output samples here.
+   ** No loop unrolling is used. */
+  blkCnt = blockSize % 0x4U;
+
+#else
+
+  /* Run the below code for Cortex-M0 */
+
+  /* Initialize blkCnt with number of samples */
+  blkCnt = blockSize;
+
+#endif /* #if defined (ARM_MATH_DSP) */
+
+
+  while (blkCnt > 0U)
+  {
+    /* C = A * B */
+    /* Multiply the inputs and store the result in the destination buffer */
+    *pDst++ = (q7_t) __SSAT((((q15_t) (*pSrcA++) * (*pSrcB++) + NN_ROUND(out_shift)) >> out_shift), 8);
+
+    /* Decrement the blockSize loop counter */
+    blkCnt--;
+  }
+}
+
+/**
+ * @} end of NNBasicMath group
+ */

Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_nntables.c

@@ -0,0 +1,297 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_nntables.c
+ * Description:  Converts the elements of the Q7 vector to Q15 vector without left-shift
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**
+ * @brief tables for various activation functions
+ *
+ * This file include the declaration of common tables.
+ * Most of them are used for activation functions 
+ *
+ * Assumption:
+ * Unified table: input is 3.x format, i.e, range of [-8, 8)
+ * sigmoid(8) = 0.9996646498695336
+ * tanh(8) = 0.9999997749296758
+ * The accuracy here should be good enough
+ *
+ * 2-stage HL table: 
+ *
+ * The entire input range is divided into two parts:
+ *
+ * Low range table: 0x000x xxxx or 0x111x xxxx 
+ * table entry will be the binary number excluding the first
+ * two digits, i.e., 0x0x xxxx or 0x1x xxxx
+ * 
+ *
+ *
+ * High range table 0x0010 0000 -- 0x0111 1111
+ *                  0x1000 0000 -- 0x1101 1111
+ * 
+ * For positive numbers, table entry will be
+ * 0x0010 0000 -- 0x0111 1111 minus 0x0010 0000
+ * i.e., 0x0000 0000 - 0x0101 11111
+ *
+ * same thing for the negative numbers, table entry will be
+ * 0x1000 0000 -- 0x1101 1111 minux 0x0010 0000
+ * i.e., 0x0110 0000 - 0x1011 1111
+ */
+
+const q7_t sigmoidTable_q7[256] = {
+    0x40, 0x42, 0x44, 0x46, 0x48, 0x4a, 0x4c, 0x4e,
+    0x50, 0x52, 0x53, 0x55, 0x57, 0x59, 0x5a, 0x5c,
+    0x5e, 0x5f, 0x61, 0x62, 0x63, 0x65, 0x66, 0x67,
+    0x69, 0x6a, 0x6b, 0x6c, 0x6d, 0x6e, 0x6f, 0x70,
+    0x71, 0x72, 0x72, 0x73, 0x74, 0x74, 0x75, 0x76,
+    0x76, 0x77, 0x77, 0x78, 0x78, 0x79, 0x79, 0x7a,
+    0x7a, 0x7a, 0x7b, 0x7b, 0x7b, 0x7c, 0x7c, 0x7c,
+    0x7c, 0x7c, 0x7d, 0x7d, 0x7d, 0x7d, 0x7d, 0x7e,
+    0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7e, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,
+    0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+    0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01,
+    0x01, 0x01, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02,
+    0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x04,
+    0x04, 0x04, 0x04, 0x04, 0x05, 0x05, 0x05, 0x06,
+    0x06, 0x06, 0x07, 0x07, 0x08, 0x08, 0x09, 0x09,
+    0x0a, 0x0a, 0x0b, 0x0c, 0x0c, 0x0d, 0x0e, 0x0e,
+    0x0f, 0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16,
+    0x17, 0x19, 0x1a, 0x1b, 0x1d, 0x1e, 0x1f, 0x21,
+    0x22, 0x24, 0x26, 0x27, 0x29, 0x2b, 0x2d, 0x2e,
+    0x30, 0x32, 0x34, 0x36, 0x38, 0x3a, 0x3c, 0x3e,
+};
+
+const q15_t sigmoidTable_q15[256] = {
+    0x4000, 0x4200, 0x43ff, 0x45fc, 0x47f5, 0x49eb, 0x4bdc, 0x4dc8,
+    0x4fad, 0x518a, 0x5360, 0x552c, 0x56ef, 0x58a8, 0x5a57, 0x5bfb,
+    0x5d93, 0x5f20, 0x60a1, 0x6216, 0x637f, 0x64db, 0x662b, 0x676f,
+    0x68a6, 0x69d2, 0x6af1, 0x6c05, 0x6d0d, 0x6e09, 0x6efb, 0x6fe2,
+    0x70be, 0x7190, 0x7258, 0x7316, 0x73cc, 0x7478, 0x751b, 0x75b7,
+    0x764a, 0x76d6, 0x775b, 0x77d8, 0x784f, 0x78c0, 0x792a, 0x798f,
+    0x79ee, 0x7a48, 0x7a9d, 0x7aed, 0x7b39, 0x7b80, 0x7bc4, 0x7c03,
+    0x7c3f, 0x7c78, 0x7cad, 0x7ce0, 0x7d0f, 0x7d3c, 0x7d66, 0x7d8d,
+    0x7db3, 0x7dd6, 0x7df7, 0x7e16, 0x7e33, 0x7e4f, 0x7e69, 0x7e81,
+    0x7e98, 0x7eae, 0x7ec2, 0x7ed5, 0x7ee7, 0x7ef8, 0x7f08, 0x7f17,
+    0x7f25, 0x7f32, 0x7f3e, 0x7f4a, 0x7f55, 0x7f5f, 0x7f69, 0x7f72,
+    0x7f7b, 0x7f83, 0x7f8a, 0x7f91, 0x7f98, 0x7f9e, 0x7fa4, 0x7faa,
+    0x7faf, 0x7fb4, 0x7fb8, 0x7fbd, 0x7fc1, 0x7fc5, 0x7fc8, 0x7fcc,
+    0x7fcf, 0x7fd2, 0x7fd5, 0x7fd7, 0x7fda, 0x7fdc, 0x7fde, 0x7fe0,
+    0x7fe2, 0x7fe4, 0x7fe6, 0x7fe7, 0x7fe9, 0x7fea, 0x7feb, 0x7fed,
+    0x7fee, 0x7fef, 0x7ff0, 0x7ff1, 0x7ff2, 0x7ff3, 0x7ff4, 0x7ff4,
+    0x000b, 0x000c, 0x000c, 0x000d, 0x000e, 0x000f, 0x0010, 0x0011,
+    0x0012, 0x0013, 0x0015, 0x0016, 0x0017, 0x0019, 0x001a, 0x001c,
+    0x001e, 0x0020, 0x0022, 0x0024, 0x0026, 0x0029, 0x002b, 0x002e,
+    0x0031, 0x0034, 0x0038, 0x003b, 0x003f, 0x0043, 0x0048, 0x004c,
+    0x0051, 0x0056, 0x005c, 0x0062, 0x0068, 0x006f, 0x0076, 0x007d,
+    0x0085, 0x008e, 0x0097, 0x00a1, 0x00ab, 0x00b6, 0x00c2, 0x00ce,
+    0x00db, 0x00e9, 0x00f8, 0x0108, 0x0119, 0x012b, 0x013e, 0x0152,
+    0x0168, 0x017f, 0x0197, 0x01b1, 0x01cd, 0x01ea, 0x0209, 0x022a,
+    0x024d, 0x0273, 0x029a, 0x02c4, 0x02f1, 0x0320, 0x0353, 0x0388,
+    0x03c1, 0x03fd, 0x043c, 0x0480, 0x04c7, 0x0513, 0x0563, 0x05b8,
+    0x0612, 0x0671, 0x06d6, 0x0740, 0x07b1, 0x0828, 0x08a5, 0x092a,
+    0x09b6, 0x0a49, 0x0ae5, 0x0b88, 0x0c34, 0x0cea, 0x0da8, 0x0e70,
+    0x0f42, 0x101e, 0x1105, 0x11f7, 0x12f3, 0x13fb, 0x150f, 0x162e,
+    0x175a, 0x1891, 0x19d5, 0x1b25, 0x1c81, 0x1dea, 0x1f5f, 0x20e0,
+    0x226d, 0x2405, 0x25a9, 0x2758, 0x2911, 0x2ad4, 0x2ca0, 0x2e76,
+    0x3053, 0x3238, 0x3424, 0x3615, 0x380b, 0x3a04, 0x3c01, 0x3e00,
+};
+
+const q15_t sigmoidLTable_q15[128] = {
+    0x4000, 0x4100, 0x4200, 0x42ff, 0x43ff, 0x44fd, 0x45fc, 0x46f9,
+    0x47f5, 0x48f1, 0x49eb, 0x4ae5, 0x4bdc, 0x4cd3, 0x4dc8, 0x4ebb,
+    0x4fad, 0x509c, 0x518a, 0x5276, 0x5360, 0x5447, 0x552c, 0x560f,
+    0x56ef, 0x57cd, 0x58a8, 0x5981, 0x5a57, 0x5b2a, 0x5bfb, 0x5cc9,
+    0x5d93, 0x5e5b, 0x5f20, 0x5fe2, 0x60a1, 0x615d, 0x6216, 0x62cc,
+    0x637f, 0x642e, 0x64db, 0x6584, 0x662b, 0x66ce, 0x676f, 0x680c,
+    0x68a6, 0x693d, 0x69d2, 0x6a63, 0x6af1, 0x6b7c, 0x6c05, 0x6c8a,
+    0x6d0d, 0x6d8d, 0x6e09, 0x6e84, 0x6efb, 0x6f70, 0x6fe2, 0x7051,
+    0x0f42, 0x0faf, 0x101e, 0x1090, 0x1105, 0x117c, 0x11f7, 0x1273,
+    0x12f3, 0x1376, 0x13fb, 0x1484, 0x150f, 0x159d, 0x162e, 0x16c3,
+    0x175a, 0x17f4, 0x1891, 0x1932, 0x19d5, 0x1a7c, 0x1b25, 0x1bd2,
+    0x1c81, 0x1d34, 0x1dea, 0x1ea3, 0x1f5f, 0x201e, 0x20e0, 0x21a5,
+    0x226d, 0x2337, 0x2405, 0x24d6, 0x25a9, 0x267f, 0x2758, 0x2833,
+    0x2911, 0x29f1, 0x2ad4, 0x2bb9, 0x2ca0, 0x2d8a, 0x2e76, 0x2f64,
+    0x3053, 0x3145, 0x3238, 0x332d, 0x3424, 0x351b, 0x3615, 0x370f,
+    0x380b, 0x3907, 0x3a04, 0x3b03, 0x3c01, 0x3d01, 0x3e00, 0x3f00,
+};
+
+const q15_t sigmoidHTable_q15[192] = {
+    0x70be, 0x7190, 0x7258, 0x7316, 0x73cc, 0x7478, 0x751b, 0x75b7,
+    0x764a, 0x76d6, 0x775b, 0x77d8, 0x784f, 0x78c0, 0x792a, 0x798f,
+    0x79ee, 0x7a48, 0x7a9d, 0x7aed, 0x7b39, 0x7b80, 0x7bc4, 0x7c03,
+    0x7c3f, 0x7c78, 0x7cad, 0x7ce0, 0x7d0f, 0x7d3c, 0x7d66, 0x7d8d,
+    0x7db3, 0x7dd6, 0x7df7, 0x7e16, 0x7e33, 0x7e4f, 0x7e69, 0x7e81,
+    0x7e98, 0x7eae, 0x7ec2, 0x7ed5, 0x7ee7, 0x7ef8, 0x7f08, 0x7f17,
+    0x7f25, 0x7f32, 0x7f3e, 0x7f4a, 0x7f55, 0x7f5f, 0x7f69, 0x7f72,
+    0x7f7b, 0x7f83, 0x7f8a, 0x7f91, 0x7f98, 0x7f9e, 0x7fa4, 0x7faa,
+    0x7faf, 0x7fb4, 0x7fb8, 0x7fbd, 0x7fc1, 0x7fc5, 0x7fc8, 0x7fcc,
+    0x7fcf, 0x7fd2, 0x7fd5, 0x7fd7, 0x7fda, 0x7fdc, 0x7fde, 0x7fe0,
+    0x7fe2, 0x7fe4, 0x7fe6, 0x7fe7, 0x7fe9, 0x7fea, 0x7feb, 0x7fed,
+    0x7fee, 0x7fef, 0x7ff0, 0x7ff1, 0x7ff2, 0x7ff3, 0x7ff4, 0x7ff4,
+    0x000b, 0x000c, 0x000c, 0x000d, 0x000e, 0x000f, 0x0010, 0x0011,
+    0x0012, 0x0013, 0x0015, 0x0016, 0x0017, 0x0019, 0x001a, 0x001c,
+    0x001e, 0x0020, 0x0022, 0x0024, 0x0026, 0x0029, 0x002b, 0x002e,
+    0x0031, 0x0034, 0x0038, 0x003b, 0x003f, 0x0043, 0x0048, 0x004c,
+    0x0051, 0x0056, 0x005c, 0x0062, 0x0068, 0x006f, 0x0076, 0x007d,
+    0x0085, 0x008e, 0x0097, 0x00a1, 0x00ab, 0x00b6, 0x00c2, 0x00ce,
+    0x00db, 0x00e9, 0x00f8, 0x0108, 0x0119, 0x012b, 0x013e, 0x0152,
+    0x0168, 0x017f, 0x0197, 0x01b1, 0x01cd, 0x01ea, 0x0209, 0x022a,
+    0x024d, 0x0273, 0x029a, 0x02c4, 0x02f1, 0x0320, 0x0353, 0x0388,
+    0x03c1, 0x03fd, 0x043c, 0x0480, 0x04c7, 0x0513, 0x0563, 0x05b8,
+    0x0612, 0x0671, 0x06d6, 0x0740, 0x07b1, 0x0828, 0x08a5, 0x092a,
+    0x09b6, 0x0a49, 0x0ae5, 0x0b88, 0x0c34, 0x0cea, 0x0da8, 0x0e70,
+};
+
+const q7_t tanhTable_q7[256] = {
+    0x00, 0x08, 0x10, 0x18, 0x1f, 0x27, 0x2e, 0x35,
+    0x3b, 0x41, 0x47, 0x4c, 0x51, 0x56, 0x5a, 0x5e,
+    0x61, 0x65, 0x68, 0x6a, 0x6d, 0x6f, 0x71, 0x72,
+    0x74, 0x75, 0x76, 0x78, 0x78, 0x79, 0x7a, 0x7b,
+    0x7b, 0x7c, 0x7c, 0x7d, 0x7d, 0x7e, 0x7e, 0x7e,
+    0x7e, 0x7e, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f, 0x7f,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80,
+    0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x80, 0x81,
+    0x81, 0x81, 0x81, 0x81, 0x81, 0x81, 0x81, 0x82,
+    0x82, 0x82, 0x82, 0x82, 0x83, 0x83, 0x84, 0x84,
+    0x85, 0x85, 0x86, 0x87, 0x88, 0x88, 0x8a, 0x8b,
+    0x8c, 0x8e, 0x8f, 0x91, 0x93, 0x96, 0x98, 0x9b,
+    0x9f, 0xa2, 0xa6, 0xaa, 0xaf, 0xb4, 0xb9, 0xbf,
+    0xc5, 0xcb, 0xd2, 0xd9, 0xe1, 0xe8, 0xf0, 0xf8,
+};
+
+const q15_t tanhTable_q15[256] = {
+    0x0000, 0x07fd, 0x0feb, 0x17b9, 0x1f59, 0x26bf, 0x2ddf, 0x34ae,
+    0x3b27, 0x4142, 0x46fd, 0x4c56, 0x514d, 0x55e2, 0x5a1a, 0x5df6,
+    0x617c, 0x64b0, 0x6797, 0x6a37, 0x6c95, 0x6eb5, 0x709e, 0x7254,
+    0x73dc, 0x753a, 0x7672, 0x7788, 0x787f, 0x795b, 0x7a1e, 0x7acb,
+    0x7b65, 0x7bee, 0x7c66, 0x7cd1, 0x7d30, 0x7d84, 0x7dce, 0x7e0f,
+    0x7e49, 0x7e7d, 0x7eaa, 0x7ed2, 0x7ef5, 0x7f14, 0x7f30, 0x7f48,
+    0x7f5e, 0x7f71, 0x7f82, 0x7f91, 0x7f9e, 0x7fa9, 0x7fb3, 0x7fbc,
+    0x7fc4, 0x7fcb, 0x7fd1, 0x7fd7, 0x7fdc, 0x7fe0, 0x7fe4, 0x7fe7,
+    0x7fea, 0x7fed, 0x7fef, 0x7ff1, 0x7ff3, 0x7ff4, 0x7ff6, 0x7ff7,
+    0x7ff8, 0x7ff9, 0x7ffa, 0x7ffa, 0x7ffb, 0x7ffc, 0x7ffc, 0x7ffd,
+    0x7ffd, 0x7ffd, 0x7ffe, 0x7ffe, 0x7ffe, 0x7ffe, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001,
+    0x8001, 0x8001, 0x8001, 0x8002, 0x8002, 0x8002, 0x8002, 0x8003,
+    0x8003, 0x8003, 0x8004, 0x8004, 0x8005, 0x8006, 0x8006, 0x8007,
+    0x8008, 0x8009, 0x800a, 0x800c, 0x800d, 0x800f, 0x8011, 0x8013,
+    0x8016, 0x8019, 0x801c, 0x8020, 0x8024, 0x8029, 0x802f, 0x8035,
+    0x803c, 0x8044, 0x804d, 0x8057, 0x8062, 0x806f, 0x807e, 0x808f,
+    0x80a2, 0x80b8, 0x80d0, 0x80ec, 0x810b, 0x812e, 0x8156, 0x8183,
+    0x81b7, 0x81f1, 0x8232, 0x827c, 0x82d0, 0x832f, 0x839a, 0x8412,
+    0x849b, 0x8535, 0x85e2, 0x86a5, 0x8781, 0x8878, 0x898e, 0x8ac6,
+    0x8c24, 0x8dac, 0x8f62, 0x914b, 0x936b, 0x95c9, 0x9869, 0x9b50,
+    0x9e84, 0xa20a, 0xa5e6, 0xaa1e, 0xaeb3, 0xb3aa, 0xb903, 0xbebe,
+    0xc4d9, 0xcb52, 0xd221, 0xd941, 0xe0a7, 0xe847, 0xf015, 0xf803,
+};
+
+const q15_t tanhLTable_q15[128] = {
+    0x0000, 0x0400, 0x07fd, 0x0bf7, 0x0feb, 0x13d7, 0x17b9, 0x1b90,
+    0x1f59, 0x2314, 0x26bf, 0x2a58, 0x2ddf, 0x3151, 0x34ae, 0x37f6,
+    0x3b27, 0x3e40, 0x4142, 0x442c, 0x46fd, 0x49b6, 0x4c56, 0x4edd,
+    0x514d, 0x53a3, 0x55e2, 0x580a, 0x5a1a, 0x5c13, 0x5df6, 0x5fc4,
+    0x617c, 0x6320, 0x64b0, 0x662d, 0x6797, 0x68f0, 0x6a37, 0x6b6e,
+    0x6c95, 0x6dac, 0x6eb5, 0x6fb0, 0x709e, 0x717f, 0x7254, 0x731e,
+    0x73dc, 0x7490, 0x753a, 0x75da, 0x7672, 0x7701, 0x7788, 0x7807,
+    0x787f, 0x78f0, 0x795b, 0x79bf, 0x7a1e, 0x7a77, 0x7acb, 0x7b1b,
+    0x849b, 0x84e5, 0x8535, 0x8589, 0x85e2, 0x8641, 0x86a5, 0x8710,
+    0x8781, 0x87f9, 0x8878, 0x88ff, 0x898e, 0x8a26, 0x8ac6, 0x8b70,
+    0x8c24, 0x8ce2, 0x8dac, 0x8e81, 0x8f62, 0x9050, 0x914b, 0x9254,
+    0x936b, 0x9492, 0x95c9, 0x9710, 0x9869, 0x99d3, 0x9b50, 0x9ce0,
+    0x9e84, 0xa03c, 0xa20a, 0xa3ed, 0xa5e6, 0xa7f6, 0xaa1e, 0xac5d,
+    0xaeb3, 0xb123, 0xb3aa, 0xb64a, 0xb903, 0xbbd4, 0xbebe, 0xc1c0,
+    0xc4d9, 0xc80a, 0xcb52, 0xceaf, 0xd221, 0xd5a8, 0xd941, 0xdcec,
+    0xe0a7, 0xe470, 0xe847, 0xec29, 0xf015, 0xf409, 0xf803, 0xfc00,
+};
+
+const q15_t tanhHTable_q15[192] = {
+    0x7b65, 0x7bee, 0x7c66, 0x7cd1, 0x7d30, 0x7d84, 0x7dce, 0x7e0f,
+    0x7e49, 0x7e7d, 0x7eaa, 0x7ed2, 0x7ef5, 0x7f14, 0x7f30, 0x7f48,
+    0x7f5e, 0x7f71, 0x7f82, 0x7f91, 0x7f9e, 0x7fa9, 0x7fb3, 0x7fbc,
+    0x7fc4, 0x7fcb, 0x7fd1, 0x7fd7, 0x7fdc, 0x7fe0, 0x7fe4, 0x7fe7,
+    0x7fea, 0x7fed, 0x7fef, 0x7ff1, 0x7ff3, 0x7ff4, 0x7ff6, 0x7ff7,
+    0x7ff8, 0x7ff9, 0x7ffa, 0x7ffa, 0x7ffb, 0x7ffc, 0x7ffc, 0x7ffd,
+    0x7ffd, 0x7ffd, 0x7ffe, 0x7ffe, 0x7ffe, 0x7ffe, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff, 0x7fff,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000, 0x8000,
+    0x8000, 0x8000, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001, 0x8001,
+    0x8001, 0x8001, 0x8001, 0x8002, 0x8002, 0x8002, 0x8002, 0x8003,
+    0x8003, 0x8003, 0x8004, 0x8004, 0x8005, 0x8006, 0x8006, 0x8007,
+    0x8008, 0x8009, 0x800a, 0x800c, 0x800d, 0x800f, 0x8011, 0x8013,
+    0x8016, 0x8019, 0x801c, 0x8020, 0x8024, 0x8029, 0x802f, 0x8035,
+    0x803c, 0x8044, 0x804d, 0x8057, 0x8062, 0x806f, 0x807e, 0x808f,
+    0x80a2, 0x80b8, 0x80d0, 0x80ec, 0x810b, 0x812e, 0x8156, 0x8183,
+    0x81b7, 0x81f1, 0x8232, 0x827c, 0x82d0, 0x832f, 0x839a, 0x8412,
+};

Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_no_shift.c

@@ -0,0 +1,134 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_q7_to_q15_no_shift.c
+ * Description:  Converts the elements of the Q7 vector to Q15 vector without left-shift
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**    
+ * @ingroup groupSupport    
+ */
+
+/**    
+ * @addtogroup nndata_convert    
+ * @{    
+ */
+
+/**    
+ * @brief Converts the elements of the Q7 vector to Q15 vector without left-shift 
+ * @param[in]       *pSrc points to the Q7 input vector    
+ * @param[out]      *pDst points to the Q15 output vector   
+ * @param[in]       blockSize length of the input vector    
+ * @return none.    
+ *    
+ * \par Description:    
+ *    
+ * The equation used for the conversion process is:    
+ *   
+ * <pre>    
+ * 	pDst[n] = (q15_t) pSrc[n];   0 <= n < blockSize.    
+ * </pre>    
+ *   
+ */
+
+void arm_q7_to_q15_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize)
+{
+    const q7_t *pIn = pSrc;     /* Src pointer */
+    uint32_t  blkCnt;           /* loop counter */
+
+#ifndef ARM_MATH_CM0_FAMILY
+    q31_t     in;
+    q31_t     in1, in2;
+    q31_t     out1, out2;
+
+    /* Run the below code for Cortex-M4 and Cortex-M3 */
+
+    /*loop Unrolling */
+    blkCnt = blockSize >> 2u;
+
+    /* First part of the processing with loop unrolling.  Compute 4 outputs at a time.    
+     ** a second loop below computes the remaining 1 to 3 samples. */
+    while (blkCnt > 0u)
+    {
+        /* C = (q15_t) A << 8 */
+        /* convert from q7 to q15 and then store the results in the destination buffer */
+        in = *__SIMD32(pIn)++;
+
+        /* rotatate in by 8 and extend two q7_t values to q15_t values */
+        in1 = __SXTB16(__ROR(in, 8));
+
+        /* extend remainig two q7_t values to q15_t values */
+        in2 = __SXTB16(in);
+
+#ifndef ARM_MATH_BIG_ENDIAN
+
+        out2 = __PKHTB(in1, in2, 16);
+        out1 = __PKHBT(in2, in1, 16);
+
+#else
+
+        out1 = __PKHTB(in1, in2, 16);
+        out2 = __PKHBT(in2, in1, 16);
+
+#endif
+
+        *__SIMD32(pDst)++ = out1;
+        *__SIMD32(pDst)++ = out2;
+
+        /* Decrement the loop counter */
+        blkCnt--;
+    }
+
+    /* If the blockSize is not a multiple of 4, compute any remaining output samples here.    
+     ** No loop unrolling is used. */
+    blkCnt = blockSize % 0x4u;
+
+#else
+
+    /* Run the below code for Cortex-M0 */
+
+    /* Loop over blockSize number of values */
+    blkCnt = blockSize;
+
+#endif                          /* #ifndef ARM_MATH_CM0_FAMILY */
+
+    while (blkCnt > 0u)
+    {
+        /* C = (q15_t) A << 8 */
+        /* convert from q7 to q15 and then store the results in the destination buffer */
+        *pDst++ = (q15_t) * pIn++;
+
+        /* Decrement the loop counter */
+        blkCnt--;
+    }
+
+}
+
+/**    
+ * @} end of nndata_convert group   
+ */

Drivers/CMSIS/NN/Source/NNSupportFunctions/arm_q7_to_q15_reordered_no_shift.c

@@ -0,0 +1,145 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_q7_to_q15_reordered_no_shift.c
+ * Description:  Converts the elements of the Q7 vector to reordered Q15 vector without left-shift
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_nnsupportfunctions.h"
+
+/**    
+ * @ingroup groupSupport    
+ */
+
+/**    
+ * @addtogroup nndata_convert    
+ * @{    
+ */
+
+/**    
+ * @brief Converts the elements of the Q7 vector to reordered Q15 vector without left-shift
+ * @param[in]       *pSrc points to the Q7 input vector    
+ * @param[out]      *pDst points to the Q15 output vector   
+ * @param[in]       blockSize length of the input vector    
+ * @return none.    
+ *    
+ * @details
+ *
+ * This function does the q7 to q15 expansion with re-ordering 
+ *
+ * <pre>
+ *                          |   A1   |   A2   |   A3   |   A4   |
+ *
+ *                           0      7 8     15 16    23 24    31
+ * </pre>
+ *
+ * is converted into:
+ *
+ * <pre>
+ *  |       A1       |       A3       |   and  |       A2       |       A4       |
+ *
+ *   0             15 16            31          0             15 16            31
+ * </pre>
+ *
+ *
+ * This looks strange but is natural considering how sign-extension is done at
+ * assembly level. 
+ *
+ * The expansion of other other oprand will follow the same rule so that the end 
+ * results are the same.
+ *
+ * The tail (i.e., last (N % 4) elements) will still be in original order.
+ *   
+ */
+
+void arm_q7_to_q15_reordered_no_shift(const q7_t * pSrc, q15_t * pDst, uint32_t blockSize)
+{
+    const q7_t *pIn = pSrc;     /* Src pointer */
+    uint32_t  blkCnt;           /* loop counter */
+
+#ifndef ARM_MATH_CM0_FAMILY
+    q31_t     in;
+    q31_t     in1, in2;
+
+    /* Run the below code for Cortex-M4 and Cortex-M3 */
+
+    /*loop Unrolling */
+    blkCnt = blockSize >> 2u;
+
+    /* First part of the processing with loop unrolling.  Compute 4 outputs at a time.    
+     ** a second loop below computes the remaining 1 to 3 samples. */
+    while (blkCnt > 0u)
+    {
+        /* C = (q15_t) A << 8 */
+        /* convert from q7 to q15 and then store the results in the destination buffer */
+        in = *__SIMD32(pIn)++;
+
+        /* rotatate in by 8 and extend two q7_t values to q15_t values */
+        in1 = __SXTB16(__ROR(in, 8));
+
+        /* extend remainig two q7_t values to q15_t values */
+        in2 = __SXTB16(in);
+
+#ifndef ARM_MATH_BIG_ENDIAN
+        *__SIMD32(pDst)++ = in2;
+        *__SIMD32(pDst)++ = in1;
+#else
+        *__SIMD32(pDst)++ = in1;
+        *__SIMD32(pDst)++ = in2;
+#endif
+
+        /* Decrement the loop counter */
+        blkCnt--;
+    }
+
+    /* If the blockSize is not a multiple of 4, compute any remaining output samples here.    
+     ** No loop unrolling is used. */
+    blkCnt = blockSize % 0x4u;
+
+#else
+
+    /* Run the below code for Cortex-M0 */
+
+    /* Loop over blockSize number of values */
+    blkCnt = blockSize;
+
+#endif                          /* #ifndef ARM_MATH_CM0_FAMILY */
+
+    while (blkCnt > 0u)
+    {
+        /* C = (q15_t) A << 8 */
+        /* convert from q7 to q15 and then store the results in the destination buffer */
+        *pDst++ = (q15_t) * pIn++;
+
+        /* Decrement the loop counter */
+        blkCnt--;
+    }
+
+}
+
+/**    
+ * @} end of q7_to_x group    
+ */

Drivers/CMSIS/NN/Source/PoolingFunctions/arm_pool_q7_HWC.c

@@ -0,0 +1,460 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_pool_q7_HWC.c
+ * Description:  Pooling function implementations
+ *
+ * $Date:        17. January 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+#if defined (ARM_MATH_DSP)
+
+/**
+ * @brief A few utility functions used by pooling functions
+ *
+ * 
+ */
+
+static void buffer_scale_back_q15_to_q7(q15_t * buffer, q7_t * target, uint16_t length, uint16_t scale)
+{
+    int       i;
+
+    for (i = 0; i < length; i++)
+    {
+        target[i] = (q7_t) (buffer[i] / scale);
+    }
+}
+
+static void compare_and_replace_if_larger_q7(q7_t * base,   // base data
+                                             const q7_t * target,   // compare target
+                                             const uint16_t length  // data length
+    )
+{
+    q7_t     *pIn = base;
+    const q7_t     *pCom = target;
+    union arm_nnword in;
+    union arm_nnword com;
+    uint16_t  cnt = length >> 2;
+
+    while (cnt > 0u)
+    {
+        in.word = *__SIMD32(pIn);
+        com.word = *__SIMD32(pCom)++;
+
+        // if version
+        if (com.bytes[0] > in.bytes[0])
+            in.bytes[0] = com.bytes[0];
+        if (com.bytes[1] > in.bytes[1])
+            in.bytes[1] = com.bytes[1];
+        if (com.bytes[2] > in.bytes[2])
+            in.bytes[2] = com.bytes[2];
+        if (com.bytes[3] > in.bytes[3])
+            in.bytes[3] = com.bytes[3];
+
+        *__SIMD32(pIn)++ = in.word;
+
+        cnt--;
+    }
+
+    cnt = length & 0x3;
+    while (cnt > 0u)
+    {
+      if (*pCom > *pIn)
+      {
+        *pIn = *pCom;
+      }
+      pIn++;
+      pCom++;
+      cnt--;
+    }
+}
+
+static void accumulate_q7_to_q15(q15_t * base, q7_t * target, const uint16_t length)
+{
+    q15_t    *pCnt = base;
+    q7_t     *pV = target;
+    q31_t     v1, v2, vo1, vo2;
+    uint16_t  cnt = length >> 2;
+    q31_t     in;
+
+    while (cnt > 0u)
+    {
+        q31_t     value = *__SIMD32(pV)++;
+        v1 = __SXTB16(__ROR(value, 8));
+        v2 = __SXTB16(value);
+#ifndef ARM_MATH_BIG_ENDIAN
+
+        vo2 = __PKHTB(v1, v2, 16);
+        vo1 = __PKHBT(v2, v1, 16);
+
+#else
+
+        vo1 = __PKHTB(v1, v2, 16);
+        vo2 = __PKHBT(v2, v1, 16);
+
+#endif
+
+        in = *__SIMD32(pCnt);
+        *__SIMD32(pCnt)++ = __QADD16(vo1, in);
+
+        in = *__SIMD32(pCnt);
+        *__SIMD32(pCnt)++ = __QADD16(vo2, in);
+
+        cnt--;
+    }
+    cnt = length & 0x3;
+    while (cnt > 0u)
+    {
+        *pCnt++ += *pV++;
+        cnt--;
+    }
+}
+
+#endif                          // ARM_MATH_DSP
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Pooling
+ * @{
+ */
+
+  /**
+   * @brief Q7 max pooling function
+   * @param[in, out]  Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @return none.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size:  0
+   *
+   * The pooling function is implemented as split x-pooling then
+   * y-pooling.
+   *
+   * This pooling function is input-destructive. Input data is undefined
+   * after calling this function.
+   *
+   */
+
+void
+arm_maxpool_q7_HWC(q7_t * Im_in,
+                   const uint16_t dim_im_in,
+                   const uint16_t ch_im_in,
+                   const uint16_t dim_kernel,
+                   const uint16_t padding,
+                   const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    int16_t   i_x, i_y;
+
+    /* first does the pooling along x axis */
+    for (i_y = 0; i_y < dim_im_in; i_y++)
+    {
+
+        for (i_x = 0; i_x < dim_im_out; i_x++)
+        {
+            /* for each output pixel */
+            q7_t     *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in;
+            q7_t     *win_start;
+            q7_t     *win_stop;
+            if (i_x * stride - padding < 0)
+            {
+                win_start = target;
+            } else
+            {
+                win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in;
+            }
+
+            if (i_x * stride - padding + dim_kernel >= dim_im_in)
+            {
+                win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in;
+            } else
+            {
+                win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in;
+            }
+
+            /* first step is to copy over initial data */
+            /* arm_copy_q7(win_start, target, ch_im_in); */
+            memmove(target, win_start, ch_im_in);
+
+            /* start the max operation from the second part */
+            win_start += ch_im_in;
+            for (; win_start < win_stop; win_start += ch_im_in)
+            {
+                compare_and_replace_if_larger_q7(target, win_start, ch_im_in);
+            }
+        }
+    }
+
+    /* then does the pooling along y axis */
+    for (i_y = 0; i_y < dim_im_out; i_y++)
+    {
+
+        /* for each output row */
+        q7_t     *target = Im_out + i_y * dim_im_out * ch_im_in;
+        q7_t     *row_start;
+        q7_t     *row_end;
+        /* setting the starting row */
+        if (i_y * stride - padding < 0)
+        {
+            row_start = Im_in;
+        } else
+        {
+            row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in;
+        }
+        /* setting the stopping row */
+        if (i_y * stride - padding + dim_kernel >= dim_im_in)
+        {
+            row_end = Im_in + dim_im_in * dim_im_in * ch_im_in;
+        } else
+        {
+            row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in;
+        }
+
+        /* copy over the first row */
+        /* arm_copy_q7(row_start, target, dim_im_out * ch_im_in); */
+        memmove(target, row_start, dim_im_out * ch_im_in);
+
+        /* move over to next row */
+        row_start += ch_im_in * dim_im_in;
+
+        for (; row_start < row_end; row_start += dim_im_in * ch_im_in)
+        {
+            compare_and_replace_if_larger_q7(target, row_start, dim_im_out * ch_im_in);
+        }
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    int16_t   i_ch_in, i_x, i_y;
+    int16_t   k_x, k_y;
+
+    for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+    {
+        for (i_y = 0; i_y < dim_im_out; i_y++)
+        {
+            for (i_x = 0; i_x < dim_im_out; i_x++)
+            {
+                int       max = -129;
+                for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+                {
+                    for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+                    {
+                        if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+                        {
+                            if (Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)] > max)
+                            {
+                                max = Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+                            }
+                        }
+                    }
+                }
+                Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = max;
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+}
+
+  /**
+   * @brief Q7 average pooling function
+   * @param[in,out]   Im_in       pointer to input tensor
+   * @param[in]       dim_im_in   input tensor dimention
+   * @param[in]       ch_im_in    number of input tensor channels
+   * @param[in]       dim_kernel  filter kernel size
+   * @param[in]       padding     padding sizes
+   * @param[in]       stride      convolution stride
+   * @param[in]       dim_im_out  output tensor dimension
+   * @param[in,out]   bufferA     pointer to buffer space for input
+   * @param[in,out]   Im_out      pointer to output tensor
+   * @return none.
+   *
+   * @details
+   *
+   * <b>Buffer size:</b>
+   *
+   * bufferA size:  2*dim_im_out*ch_im_in
+   *
+   * The pooling function is implemented as split x-pooling then
+   * y-pooling.
+   *
+   * This pooling function is input-destructive. Input data is undefined
+   * after calling this function.
+   *
+   */
+
+void
+arm_avepool_q7_HWC(q7_t * Im_in,
+                   const uint16_t dim_im_in,
+                   const uint16_t ch_im_in,
+                   const uint16_t dim_kernel,
+                   const uint16_t padding,
+                   const uint16_t stride, const uint16_t dim_im_out, q7_t * bufferA, q7_t * Im_out)
+{
+
+#if defined (ARM_MATH_DSP)
+    /* Run the following code for Cortex-M4 and Cortex-M7 */
+
+    q15_t    *buffer = (q15_t *) bufferA;
+    int16_t   i_x, i_y;
+    int16_t   count = 0;
+
+    /* first does the pooling along x axis */
+    for (i_y = 0; i_y < dim_im_in; i_y++)
+    {
+
+        for (i_x = 0; i_x < dim_im_out; i_x++)
+        {
+            /* for each output pixel */
+            q7_t     *target = Im_in + (i_y * dim_im_in + i_x) * ch_im_in;
+            q7_t     *win_start;
+            q7_t     *win_stop;
+            if (i_x * stride - padding < 0)
+            {
+                win_start = target;
+            } else
+            {
+                win_start = Im_in + (i_y * dim_im_in + i_x * stride - padding) * ch_im_in;
+            }
+
+            if (i_x * stride - padding + dim_kernel >= dim_im_in)
+            {
+                win_stop = Im_in + (i_y * dim_im_in + dim_im_in) * ch_im_in;
+            } else
+            {
+                win_stop = Im_in + (i_y * dim_im_in + i_x * stride - padding + dim_kernel) * ch_im_in;
+            }
+
+            /* first step is to copy over initial data */
+            arm_q7_to_q15_no_shift(win_start, buffer, ch_im_in);
+            count = 1;
+
+            /* start the max operation from the second part */
+            win_start += ch_im_in;
+            for (; win_start < win_stop; win_start += ch_im_in)
+            {
+                accumulate_q7_to_q15(buffer, win_start, ch_im_in);
+                count++;
+            }
+            buffer_scale_back_q15_to_q7(buffer, target, ch_im_in, count);
+        }
+    }
+
+    /* then does the pooling along y axis */
+    for (i_y = 0; i_y < dim_im_out; i_y++)
+    {
+        /* for each output row */
+        q7_t     *target = Im_out + i_y * dim_im_out * ch_im_in;
+        q7_t     *row_start;
+        q7_t     *row_end;
+        /* setting the starting row */
+        if (i_y * stride - padding < 0)
+        {
+            row_start = Im_in;
+        } else
+        {
+            row_start = Im_in + (i_y * stride - padding) * dim_im_in * ch_im_in;
+        }
+        /* setting the stopping row */
+        if (i_y * stride - padding + dim_kernel >= dim_im_in)
+        {
+            row_end = Im_in + dim_im_in * dim_im_in * ch_im_in;
+        } else
+        {
+            row_end = Im_in + (i_y * stride - padding + dim_kernel) * dim_im_in * ch_im_in;
+        }
+
+        /* copy over the first row */
+        arm_q7_to_q15_no_shift(row_start, buffer, dim_im_out * ch_im_in);
+        count = 1;
+
+        /* move over to next row */
+        row_start += ch_im_in * dim_im_in;
+
+        for (; row_start < row_end; row_start += dim_im_in * ch_im_in)
+        {
+            accumulate_q7_to_q15(buffer, row_start, dim_im_out * ch_im_in);
+            count++;
+        }
+        buffer_scale_back_q15_to_q7(buffer, target, dim_im_out * ch_im_in, count);
+    }
+
+#else
+    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
+
+    int16_t   i_ch_in, i_x, i_y;
+    int16_t   k_x, k_y;
+
+    for (i_ch_in = 0; i_ch_in < ch_im_in; i_ch_in++)
+    {
+        for (i_y = 0; i_y < dim_im_out; i_y++)
+        {
+            for (i_x = 0; i_x < dim_im_out; i_x++)
+            {
+                int       sum = 0;
+                int       count = 0;
+                for (k_y = i_y * stride - padding; k_y < i_y * stride - padding + dim_kernel; k_y++)
+                {
+                    for (k_x = i_x * stride - padding; k_x < i_x * stride - padding + dim_kernel; k_x++)
+                    {
+                        if (k_y >= 0 && k_x >= 0 && k_y < dim_im_in && k_x < dim_im_in)
+                        {
+                            sum += Im_in[i_ch_in + ch_im_in * (k_x + k_y * dim_im_in)];
+                            count++;
+                        }
+                    }
+                }
+                Im_out[i_ch_in + ch_im_in * (i_x + i_y * dim_im_out)] = sum / count;
+            }
+        }
+    }
+
+#endif                          /* ARM_MATH_DSP */
+
+}
+
+/**
+ * @} end of Pooling group
+ */

Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q15.c

@@ -0,0 +1,120 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_softmax_q15.c
+ * Description:  Q15 softmax function
+ *
+ * $Date:        20. February 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Softmax
+ * @{
+ */
+
+  /**
+   * @brief Q15 softmax function
+   * @param[in]       vec_in      pointer to input vector
+   * @param[in]       dim_vec     input vector dimention
+   * @param[out]      p_out       pointer to output vector
+   * @return none.
+   *
+   * @details
+   *
+   *  Here, instead of typical e based softmax, we use
+   *  2-based softmax, i.e.,:
+   *
+   *  y_i = 2^(x_i) / sum(2^x_j)
+   *
+   *  The relative output will be different here.
+   *  But mathematically, the gradient will be the same
+   *  with a log(2) scaling factor.
+   *
+   */
+
+void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out)
+{
+    q31_t     sum;
+    int16_t   i;
+    uint8_t   shift;
+    q31_t     base;
+    base = -1 * 0x100000;
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > base)
+        {
+            base = vec_in[i];
+        }
+    }
+
+    /* we ignore really small values  
+     * anyway, they will be 0 after shrinking
+     * to q15_t
+     */
+    base = base - 16;
+
+    sum = 0;
+
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > base)
+        {
+            shift = (uint8_t)__USAT(vec_in[i] - base, 5);
+            sum += 0x1 << shift;
+        }
+    }
+
+    /* This is effectively (0x1 << 32) / sum */
+    int64_t div_base = 0x100000000LL;
+    int output_base = (int32_t)(div_base / sum);
+
+    /* Final confidence will be output_base >> ( 17 - (vec_in[i] - base) )
+     * so 32768 (0x1<<15) -> 100% confidence when sum = 0x1 << 16, output_base = 0x1 << 16
+     * and vec_in[i]-base = 16
+     */
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > base) 
+        {
+            /* Here minimum value of 17+base-vec[i] will be 1 */
+            shift = (uint8_t)__USAT(17+base-vec_in[i], 5);
+            p_out[i] = (q15_t) __SSAT((output_base >> shift), 16);
+        } else
+        {
+            p_out[i] = 0;
+        }
+    }
+
+}
+
+/**
+ * @} end of Softmax group
+ */

Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c

@@ -0,0 +1,121 @@
+/*
+ * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the License); you may
+ * not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an AS IS BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+/* ----------------------------------------------------------------------
+ * Project:      CMSIS NN Library
+ * Title:        arm_softmax_q7.c
+ * Description:  Q7 softmax function
+ *
+ * $Date:        20. February 2018
+ * $Revision:    V.1.0.0
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#include "arm_math.h"
+#include "arm_nnfunctions.h"
+
+/**
+ *  @ingroup groupNN
+ */
+
+/**
+ * @addtogroup Softmax
+ * @{
+ */
+
+  /**
+   * @brief Q7 softmax function
+   * @param[in]       vec_in      pointer to input vector
+   * @param[in]       dim_vec     input vector dimention
+   * @param[out]      p_out       pointer to output vector
+   * @return none.
+   *
+   * @details
+   *
+   *  Here, instead of typical natural logarithm e based softmax, we use
+   *  2-based softmax here, i.e.,:
+   * 
+   *  y_i = 2^(x_i) / sum(2^x_j)
+   *
+   *  The relative output will be different here.
+   *  But mathematically, the gradient will be the same
+   *  with a log(2) scaling factor.
+   *
+   */
+
+void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out)
+{
+    q31_t     sum;
+    int16_t   i;
+    uint8_t   shift;
+    q15_t     base;
+    base = -257;
+
+    /* We first search for the maximum */
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > base)
+        {
+            base = vec_in[i];
+        }
+    }
+
+    /* 
+     * So the base is set to max-8, meaning 
+     * that we ignore really small values. 
+     * anyway, they will be 0 after shrinking to q7_t.
+     */
+    base = base - 8;
+
+    sum = 0;
+
+    for (i = 0; i < dim_vec; i++)
+    {
+        if (vec_in[i] > base) 
+        {
+            shift = (uint8_t)__USAT(vec_in[i] - base, 5);
+            sum += 0x1 << shift;
+        }
+    }
+
+    /* This is effectively (0x1 << 20) / sum */
+    int output_base = 0x100000 / sum;
+
+    /* 
+     * Final confidence will be output_base >> ( 13 - (vec_in[i] - base) )
+     * so 128 (0x1<<7) -> 100% confidence when sum = 0x1 << 8, output_base = 0x1 << 12 
+     * and vec_in[i]-base = 8
+     */
+    for (i = 0; i < dim_vec; i++) 
+    {
+        if (vec_in[i] > base) 
+        {
+            /* Here minimum value of 13+base-vec_in[i] will be 5 */
+            shift = (uint8_t)__USAT(13+base-vec_in[i], 5);
+            p_out[i] = (q7_t) __SSAT((output_base >> shift), 8);
+        } else {
+            p_out[i] = 0;
+        }
+    }
+}
+
+/**
+ * @} end of Softmax group
+ */