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/*

 * Copyright (C) 2010-2018 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_mat_q7_vec_q15.c

 * Description:  Mixed Q15-Q7 fully-connected layer function

 *

 * $Date:        17. January 2018

 * $Revision:    V.1.0.0

 *

 * Target Processor:  Cortex-M cores

 *

 * -------------------------------------------------------------------- */

#include "arm_math.h"

#include "arm_nnfunctions.h"

/**

 *  @ingroup groupNN

 */

/**

 * @addtogroup FC

 * @{

 */

  /**

   * @brief Mixed Q15-Q7 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

   *

   *  Q7_Q15 version of the fully connected layer

   *

   *  Weights are in q7_t and Activations are in q15_t

   *

   */

arm_status

arm_fully_connected_mat_q7_vec_q15(const q15_t * pV,

                                   const q7_t * pM,

                                   const uint16_t dim_vec,

                                   const uint16_t num_of_rows,

                                   const uint16_t bias_shift,

                                   const uint16_t out_shift,

                                   const q7_t * bias,

                                   q15_t * pOut,

                                   q15_t * vec_buffer)

{

#if defined (ARM_MATH_DSP)

    /* Run the following code for Cortex-M4 and Cortex-M7 */

    const q7_t *pB = pM;

    const q7_t *pB2;

    q15_t    *pO = pOut;

    const q7_t *pBias = bias;

    const q15_t *pA = pV;

    uint16_t  rowCnt = num_of_rows >> 1;

    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     inV, inM11, inM12, inM21, inM22;

            pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);

            pB2 = (q7_t *) read_and_pad((void *)pB2, &inM21, &inM22);

            inV = *__SIMD32(pA)++;

            sum = __SMLAD(inV, inM11, sum);

            sum2 = __SMLAD(inV, inM21, sum2);

            inV = *__SIMD32(pA)++;

            sum = __SMLAD(inV, inM12, sum);

            sum2 = __SMLAD(inV, inM22, sum2);

            colCnt--;

        }

        colCnt = dim_vec & 0x3;

        while (colCnt)

        {

            q15_t     inV = *pA++;

            q7_t      inM = *pB++;

            q7_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 += dim_vec;

        rowCnt--;

    }

    /* left-over part of the rows */

    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, inV2, inM11, inM12;

            pB = (q7_t *) read_and_pad((void *)pB, &inM11, &inM12);

            inV1 = *__SIMD32(pA)++;

            sum = __SMLAD(inV1, inM11, sum);

            inV2 = *__SIMD32(pA)++;

            sum = __SMLAD(inV2, inM12, sum);

            colCnt--;

        }

        /* left-over of the vector */

        colCnt = dim_vec & 0x3;

        while (colCnt)

        {

            q15_t     inV = *pA++;

            q7_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 ARM_MATH_SUCCESS */

    return (ARM_MATH_SUCCESS);

}

/**

 * @} end of FC group

 */