Subversion Repositories dashGPS

Rev

Rev 2 | Blame | Compare with Previous | Last modification | View Log | Download | RSS feed

  1. /*
  2.  * Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
  3.  *
  4.  * SPDX-License-Identifier: Apache-2.0
  5.  *
  6.  * Licensed under the Apache License, Version 2.0 (the License); you may
  7.  * not use this file except in compliance with the License.
  8.  * You may obtain a copy of the License at
  9.  *
  10.  * www.apache.org/licenses/LICENSE-2.0
  11.  *
  12.  * Unless required by applicable law or agreed to in writing, software
  13.  * distributed under the License is distributed on an AS IS BASIS, WITHOUT
  14.  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  15.  * See the License for the specific language governing permissions and
  16.  * limitations under the License.
  17.  */
  18.  
  19. /* ----------------------------------------------------------------------
  20.  * Project:      CMSIS NN Library
  21.  * Title:        arm_softmax_q15.c
  22.  * Description:  Q15 softmax function
  23.  *
  24.  * $Date:        20. February 2018
  25.  * $Revision:    V.1.0.0
  26.  *
  27.  * Target Processor:  Cortex-M cores
  28.  *
  29.  * -------------------------------------------------------------------- */
  30.  
  31. #include "arm_math.h"
  32. #include "arm_nnfunctions.h"
  33.  
  34. /**
  35.  *  @ingroup groupNN
  36.  */
  37.  
  38. /**
  39.  * @addtogroup Softmax
  40.  * @{
  41.  */
  42.  
  43.   /**
  44.    * @brief Q15 softmax function
  45.    * @param[in]       vec_in      pointer to input vector
  46.    * @param[in]       dim_vec     input vector dimention
  47.    * @param[out]      p_out       pointer to output vector
  48.    * @return none.
  49.    *
  50.    * @details
  51.    *
  52.    *  Here, instead of typical e based softmax, we use
  53.    *  2-based softmax, i.e.,:
  54.    *
  55.    *  y_i = 2^(x_i) / sum(2^x_j)
  56.    *
  57.    *  The relative output will be different here.
  58.    *  But mathematically, the gradient will be the same
  59.    *  with a log(2) scaling factor.
  60.    *
  61.    */
  62.  
  63. void arm_softmax_q15(const q15_t * vec_in, const uint16_t dim_vec, q15_t * p_out)
  64. {
  65.     q31_t     sum;
  66.     int16_t   i;
  67.     uint8_t   shift;
  68.     q31_t     base;
  69.     base = -1 * 0x100000;
  70.     for (i = 0; i < dim_vec; i++)
  71.     {
  72.         if (vec_in[i] > base)
  73.         {
  74.             base = vec_in[i];
  75.         }
  76.     }
  77.  
  78.     /* we ignore really small values  
  79.      * anyway, they will be 0 after shrinking
  80.      * to q15_t
  81.      */
  82.     base = base - 16;
  83.  
  84.     sum = 0;
  85.  
  86.     for (i = 0; i < dim_vec; i++)
  87.     {
  88.         if (vec_in[i] > base)
  89.         {
  90.             shift = (uint8_t)__USAT(vec_in[i] - base, 5);
  91.             sum += 0x1 << shift;
  92.         }
  93.     }
  94.  
  95.     /* This is effectively (0x1 << 32) / sum */
  96.     int64_t div_base = 0x100000000LL;
  97.     int output_base = (int32_t)(div_base / sum);
  98.  
  99.     /* Final confidence will be output_base >> ( 17 - (vec_in[i] - base) )
  100.      * so 32768 (0x1<<15) -> 100% confidence when sum = 0x1 << 16, output_base = 0x1 << 16
  101.      * and vec_in[i]-base = 16
  102.      */
  103.     for (i = 0; i < dim_vec; i++)
  104.     {
  105.         if (vec_in[i] > base)
  106.         {
  107.             /* Here minimum value of 17+base-vec[i] will be 1 */
  108.             shift = (uint8_t)__USAT(17+base-vec_in[i], 5);
  109.             p_out[i] = (q15_t) __SSAT((output_base >> shift), 16);
  110.         } else
  111.         {
  112.             p_out[i] = 0;
  113.         }
  114.     }
  115.  
  116. }
  117.  
  118. /**
  119.  * @} end of Softmax group
  120.  */
  121.