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diff --git a/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c b/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c
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+++ b/Drivers/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_q7.c
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+/*
+ * Copyright (C) 2010-2020 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:        09. October 2020
+ * $Revision:    V.1.0.2
+ *
+ * Target Processor:  Cortex-M cores
+ *
+ * -------------------------------------------------------------------- */
+
+#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
+ *
+ * @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 = -128;
+
+    /* 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 - (1 << 3);
+
+    sum = 0;
+
+    for (i = 0; i < dim_vec; i++)
+    {
+        shift = (uint8_t)__USAT(vec_in[i] - base, 3);
+        sum += 0x1 << shift;
+    }
+
+    /* This is effectively (0x1 << 20) / sum */
+    int output_base = (1 << 20) / sum;
+
+    for (i = 0; i < dim_vec; i++)
+    {
+
+        /* 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);
+    }
+}
+
+/**
+ * @} end of Softmax group
+ */