/*
 * 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_fully_connected_q15.c
 * Description:  Q15 basic fully-connected layer function
 *
 * $Date:        20. July 2021
 * $Revision:    V.1.1.1
 *
 * Target Processor:  Cortex-M cores
 *
 * -------------------------------------------------------------------- */

#include "arm_nnfunctions.h"
#include "arm_nnsupportfunctions.h"

/**
 *  @ingroup groupNN
 */

/**
 * @addtogroup FC
 * @{
 */

/**
 * @brief Q15 opt fully-connected layer function
 * @param[in]       pV          pointer to input vector
 * @param[in]       pM          pointer to matrix weights
 * @param[in]       dim_vec     length of the vector
 * @param[in]       num_of_rows number of rows in weight matrix
 * @param[in]       bias_shift  amount of left-shift for bias
 * @param[in]       out_shift   amount of right-shift for output
 * @param[in]       bias        pointer to bias
 * @param[in,out]   pOut        pointer to output vector
 * @param[in,out]   vec_buffer  pointer to buffer space for input
 * @return     The function returns <code>ARM_MATH_SUCCESS</code>
 *
 *
 * @details
 *
 * <b>Buffer size:</b>
 *
 * vec_buffer size: 0
 *
 */

arm_status arm_fully_connected_q15(const q15_t *pV,
                                   const q15_t *pM,
                                   const uint16_t dim_vec,
                                   const uint16_t num_of_rows,
                                   const uint16_t bias_shift,
                                   const uint16_t out_shift,
                                   const q15_t *bias,
                                   q15_t *pOut,
                                   q15_t *vec_buffer)
{
    (void)vec_buffer;
#if defined(ARM_MATH_DSP) && !defined(ARM_MATH_MVEI)
    /* Run the following code for Cortex-M4 and Cortex-M7 */

    const q15_t *pB = pM;
    const q15_t *pB2 = pB + dim_vec;
    q15_t *pO = pOut;
    const q15_t *pA;
    const q15_t *pBias = bias;
    uint16_t rowCnt = num_of_rows >> 1;

    /* this loop loops over different output */
    while (rowCnt)
    {
        q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);
        q31_t sum2 = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);

        uint16_t colCnt = dim_vec >> 2;

        pA = pV;
        pB2 = pB + dim_vec;

        while (colCnt)
        {
            q31_t inV1, inM1, inM2;
            inV1 = arm_nn_read_q15x2_ia(&pA);
            inM1 = arm_nn_read_q15x2_ia(&pB);
            sum = __SMLAD(inV1, inM1, sum);
            inM2 = arm_nn_read_q15x2_ia(&pB2);
            sum2 = __SMLAD(inV1, inM2, sum2);

            inV1 = arm_nn_read_q15x2_ia(&pA);
            inM1 = arm_nn_read_q15x2_ia(&pB);
            sum = __SMLAD(inV1, inM1, sum);
            inM2 = arm_nn_read_q15x2_ia(&pB2);
            sum2 = __SMLAD(inV1, inM2, sum2);

            colCnt--;
        }
        colCnt = dim_vec & 0x3;
        while (colCnt)
        {
            q15_t inV = *pA++;
            q15_t inM = *pB++;
            q15_t inM2 = *pB2++;

            sum += inV * inM;
            sum2 += inV * inM2;
            colCnt--;
        } /* while over colCnt */
        *pO++ = (q15_t)(__SSAT((sum >> out_shift), 16));
        *pO++ = (q15_t)(__SSAT((sum2 >> out_shift), 16));

        /* adjust the pointers and counters */
        pB = pB + dim_vec;
        rowCnt--;
    }

    rowCnt = num_of_rows & 0x1;

    while (rowCnt)
    {
        q31_t sum = ((q31_t)(*pBias++) << bias_shift) + NN_ROUND(out_shift);

        uint16_t colCnt = dim_vec >> 2;

        pA = pV;

        while (colCnt)
        {
            q31_t inV1, inM1;
            inV1 = arm_nn_read_q15x2_ia(&pA);
            inM1 = arm_nn_read_q15x2_ia(&pB);
            sum = __SMLAD(inV1, inM1, sum);

            inV1 = arm_nn_read_q15x2_ia(&pA);
            inM1 = arm_nn_read_q15x2_ia(&pB);
            sum = __SMLAD(inV1, inM1, sum);

            colCnt--;
        }

        /* left-over of the vector */
        colCnt = dim_vec & 0x3;
        while (colCnt)
        {
            q15_t inV = *pA++;
            q15_t inM = *pB++;

            sum += inV * inM;

            colCnt--;
        }

        *pO++ = (q15_t)(__SSAT((sum >> out_shift), 16));

        rowCnt--;
    }

#else
    int i, j;
    /* Run the following code as reference implementation for Cortex-M0 and Cortex-M3 */
    for (i = 0; i < num_of_rows; i++)
    {
        int ip_out = ((q31_t)(bias[i]) << bias_shift) + NN_ROUND(out_shift);
        for (j = 0; j < dim_vec; j++)
        {
            ip_out += pV[j] * pM[i * dim_vec + j];
        }
        pOut[i] = (q15_t)__SSAT((ip_out >> out_shift), 16);
    }

#endif /* ARM_MATH_DSP */

    /* Return to application */
    return (ARM_MATH_SUCCESS);
}

/**
 * @} end of FC group
 */