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.

2026-09-13 19:51:03 +02:00
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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_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
+ */