text: /*
   * Copyright (C) 2010-2020 Arm Limited or its affiliates. All rights reserved.
   * Copyright (c) 2019 Nuclei Limited. 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:      NMSIS NN Library
   * Title:        riscv_softmax_q7.c
   * Description:  Q7 softmax function
   *
   * $Date:        June 8, 2020
   * $Revision:    V.1.0.1
   *
   * Target Processor: RISC-V Cores
   *
   * -------------------------------------------------------------------- */
  
  #include "ref_functions.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 riscv_softmax_q7_ref(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 - 8;
  
      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
   */
  