1 files changed, 121 insertions, 0 deletions
diff --git a/fw/hid-dials/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c b/fw/hid-dials/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c
new file mode 100644
index 0000000..06a69e1
--- /dev/null
+++ b/fw/hid-dials/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
+ */