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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.

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pima
2026-09-13 19:51:03 +02:00
commit 7ee381de58
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Drivers/CMSIS/NN/Source/FullyConnectedFunctions/arm_fully_connected_mat_q7_vec_q15.c
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/*
* 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
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/*
* 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
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/*
* 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
+332
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@@ -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
+198
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@@ -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
+484
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@@ -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
*/