122 lines
3.0 KiB
C
122 lines
3.0 KiB
C
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/*
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* Copyright (C) 2010-2018 Arm Limited or its affiliates. All rights reserved.
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*
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* SPDX-License-Identifier: Apache-2.0
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*
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* Licensed under the Apache License, Version 2.0 (the License); you may
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* not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an AS IS BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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/* ----------------------------------------------------------------------
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* Project: CMSIS NN Library
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* Title: arm_softmax_q7.c
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* Description: Q7 softmax function
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*
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* $Date: 20. February 2018
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* $Revision: V.1.0.0
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*
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* Target Processor: Cortex-M cores
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*
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* -------------------------------------------------------------------- */
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#include "arm_math.h"
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#include "arm_nnfunctions.h"
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/**
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* @ingroup groupNN
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*/
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/**
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* @addtogroup Softmax
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* @{
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*/
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/**
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* @brief Q7 softmax function
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* @param[in] vec_in pointer to input vector
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* @param[in] dim_vec input vector dimention
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* @param[out] p_out pointer to output vector
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* @return none.
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*
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* @details
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*
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* Here, instead of typical natural logarithm e based softmax, we use
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* 2-based softmax here, i.e.,:
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*
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* y_i = 2^(x_i) / sum(2^x_j)
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*
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* The relative output will be different here.
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* But mathematically, the gradient will be the same
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* with a log(2) scaling factor.
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*
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*/
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void arm_softmax_q7(const q7_t * vec_in, const uint16_t dim_vec, q7_t * p_out)
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{
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q31_t sum;
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int16_t i;
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uint8_t shift;
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q15_t base;
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base = -257;
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/* We first search for the maximum */
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for (i = 0; i < dim_vec; i++)
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{
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if (vec_in[i] > base)
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{
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base = vec_in[i];
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}
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}
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/*
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* So the base is set to max-8, meaning
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* that we ignore really small values.
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* anyway, they will be 0 after shrinking to q7_t.
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*/
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base = base - 8;
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sum = 0;
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for (i = 0; i < dim_vec; i++)
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{
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if (vec_in[i] > base)
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{
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shift = (uint8_t)__USAT(vec_in[i] - base, 5);
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sum += 0x1 << shift;
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}
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}
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/* This is effectively (0x1 << 20) / sum */
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int output_base = 0x100000 / sum;
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/*
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* Final confidence will be output_base >> ( 13 - (vec_in[i] - base) )
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* so 128 (0x1<<7) -> 100% confidence when sum = 0x1 << 8, output_base = 0x1 << 12
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* and vec_in[i]-base = 8
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*/
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for (i = 0; i < dim_vec; i++)
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{
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if (vec_in[i] > base)
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{
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/* Here minimum value of 13+base-vec_in[i] will be 5 */
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shift = (uint8_t)__USAT(13+base-vec_in[i], 5);
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p_out[i] = (q7_t) __SSAT((output_base >> shift), 8);
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} else {
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p_out[i] = 0;
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}
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}
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}
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/**
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* @} end of Softmax group
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*/
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