src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp

/*
 * Copyright (c) 2017 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"

#include "arm_compute/core/AccessWindowTranspose.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/IAccessWindow.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/NEFixedPoint.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"

#include <arm_neon.h>
#include <cstddef>
#include <cstdint>
#include <tuple>

using namespace arm_compute;

namespace arm_compute
{
class Coordinates;
} // namespace arm_compute

namespace
{
template <bool multiply_alpha>
void vector_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
{
    const auto width_matrix_b  = static_cast<int>(output->info()->dimension(0));
    const auto in_b_stride     = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type()));
    const auto num_elems_vec_a = static_cast<int>(input0->info()->dimension(0));

    // The implementation computes 16 elements per iteration
    const int window_start_x = 16 * window.thread_id();
    const int window_step_x  = 16 * window.num_threads();
    // Make sure (window_end_x - window_start_x) is a multiple of window_step_x
    const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;

    Window win_out(window);
    win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
    win_out.set(Window::DimY, Window::Dimension(0, 1, 1));

    Window win_a(window);
    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
    win_a.set(Window::DimY, Window::Dimension(0, 0, 0));

    Window win_b;
    // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
    // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
    if(input1->info()->num_dimensions() >= 3)
    {
        win_b = window;
    }
    win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
    win_b.set(Window::DimY, Window::Dimension(0, 1, 1));

    Iterator ina(input0, win_a);
    Iterator inb(input1, win_b);
    Iterator out(output, win_out);

    execute_window_loop(win_out, [&](const Coordinates & id)
    {
        if(id.x() > width_matrix_b)
        {
            return;
        }

        float32x4_t acc0 = vdupq_n_f32(0.f);
        float32x4_t acc1 = vdupq_n_f32(0.f);
        float32x4_t acc2 = vdupq_n_f32(0.f);
        float32x4_t acc3 = vdupq_n_f32(0.f);

        auto vec_a    = reinterpret_cast<const float *>(ina.ptr());
        auto matrix_b = reinterpret_cast<const float *>(inb.ptr());

#if __arm__
        asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a)));
        asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b)));
        asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + in_b_stride)));
#endif

        auto vec_a_end_addr = vec_a + num_elems_vec_a;
        for(; vec_a <= (vec_a_end_addr - 4);)
        {
            float32x2_t a0l = vld1_f32(vec_a);

            float32x4_t b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
            float32x4_t b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
            float32x4_t b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
            float32x4_t b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);

            float32x4_t b10 = vld1q_f32(matrix_b + 0 + 1 * in_b_stride);
            float32x4_t b11 = vld1q_f32(matrix_b + 4 + 1 * in_b_stride);
            float32x4_t b12 = vld1q_f32(matrix_b + 8 + 1 * in_b_stride);
            float32x4_t b13 = vld1q_f32(matrix_b + 12 + 1 * in_b_stride);

#if __arm__
            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(vec_a)));
            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 1 * in_b_stride)));
            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 2 * in_b_stride)));
            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 3 * in_b_stride)));
            asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(matrix_b + 4 * in_b_stride)));
#endif

            acc0 = vmlaq_lane_f32(acc0, b00, a0l, 0);
            acc1 = vmlaq_lane_f32(acc1, b01, a0l, 0);
            acc2 = vmlaq_lane_f32(acc2, b02, a0l, 0);
            acc3 = vmlaq_lane_f32(acc3, b03, a0l, 0);

            acc0 = vmlaq_lane_f32(acc0, b10, a0l, 1);
            acc1 = vmlaq_lane_f32(acc1, b11, a0l, 1);
            acc2 = vmlaq_lane_f32(acc2, b12, a0l, 1);
            acc3 = vmlaq_lane_f32(acc3, b13, a0l, 1);

            vec_a += 2;
            matrix_b += 2 * in_b_stride;

            a0l = vld1_f32(vec_a);

            b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
            b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
            b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
            b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);

            b10 = vld1q_f32(matrix_b + 0 + 1 * in_b_stride);
            b11 = vld1q_f32(matrix_b + 4 + 1 * in_b_stride);
            b12 = vld1q_f32(matrix_b + 8 + 1 * in_b_stride);
            b13 = vld1q_f32(matrix_b + 12 + 1 * in_b_stride);

            acc0 = vmlaq_lane_f32(acc0, b00, a0l, 0);
            acc1 = vmlaq_lane_f32(acc1, b01, a0l, 0);
            acc2 = vmlaq_lane_f32(acc2, b02, a0l, 0);
            acc3 = vmlaq_lane_f32(acc3, b03, a0l, 0);

            acc0 = vmlaq_lane_f32(acc0, b10, a0l, 1);
            acc1 = vmlaq_lane_f32(acc1, b11, a0l, 1);
            acc2 = vmlaq_lane_f32(acc2, b12, a0l, 1);
            acc3 = vmlaq_lane_f32(acc3, b13, a0l, 1);

            vec_a += 2;
            matrix_b += 2 * in_b_stride;
        }

        for(; vec_a < vec_a_end_addr;)
        {
            const float a0 = *vec_a;

            const float32x4_t b00 = vld1q_f32(matrix_b + 0 + 0 * in_b_stride);
            const float32x4_t b01 = vld1q_f32(matrix_b + 4 + 0 * in_b_stride);
            const float32x4_t b02 = vld1q_f32(matrix_b + 8 + 0 * in_b_stride);
            const float32x4_t b03 = vld1q_f32(matrix_b + 12 + 0 * in_b_stride);

            acc0 = vmlaq_n_f32(acc0, b00, a0);
            acc1 = vmlaq_n_f32(acc1, b01, a0);
            acc2 = vmlaq_n_f32(acc2, b02, a0);
            acc3 = vmlaq_n_f32(acc3, b03, a0);

            vec_a += 1;
            matrix_b += in_b_stride;
        }

        // Multiply by the weight of matrix product (alpha)
        if(multiply_alpha)
        {
            const float32x4_t alpha_f32 = vdupq_n_f32(alpha);
            acc0                        = vmulq_f32(acc0, alpha_f32);
            acc1                        = vmulq_f32(acc1, alpha_f32);
            acc2                        = vmulq_f32(acc2, alpha_f32);
            acc3                        = vmulq_f32(acc3, alpha_f32);
        }

        const auto vec_out = reinterpret_cast<float *>(out.ptr());

        vst1q_f32(vec_out + 0, acc0);
        vst1q_f32(vec_out + 4, acc1);
        vst1q_f32(vec_out + 8, acc2);
        vst1q_f32(vec_out + 12, acc3);
    },
    ina, inb, out);
}

template <bool multiply_alpha>
void vector_matrix_multiply_qs8(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
{
    const auto width_matrix_b       = static_cast<int>(output->info()->dimension(0));
    const auto in_b_stride          = static_cast<int>(input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type()));
    const auto num_elems_vec_a      = static_cast<int>(input0->info()->dimension(0));
    const int  fixed_point_position = input0->info()->fixed_point_position();

    // The implementation computes 32 elements per iteration
    const int window_start_x = 32 * window.thread_id();
    const int window_step_x  = 32 * window.num_threads();
    // Make sure (window_end_x - window_start_x) is a multiple of window_step_x
    const int window_end_x = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;

    Window win_out(window);
    win_out.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
    win_out.set(Window::DimY, Window::Dimension(0, 1, 1));

    Window win_a(window);
    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
    win_a.set(Window::DimY, Window::Dimension(0, 0, 0));

    Window win_b;
    // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
    // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
    if(input1->info()->num_dimensions() >= 3)
    {
        win_b = window;
    }
    win_b.set(Window::DimX, Window::Dimension(window_start_x, window_end_x, window_step_x));
    win_b.set(Window::DimY, Window::Dimension(0, 1, 1));

    Iterator ina(input0, win_a);
    Iterator inb(input1, win_b);
    Iterator out(output, win_out);

    execute_window_loop(win_out, [&](const Coordinates & id)
    {
        if(id.x() > width_matrix_b)
        {
            return;
        }

        // Reset accumulators
        qint16x8_t acc00_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc01_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc02_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc03_qs16 = vdupq_n_qs16(0);

        auto vec_a    = reinterpret_cast<const qint8_t *>(ina.ptr());
        auto matrix_b = reinterpret_cast<const qint8_t *>(inb.ptr());

        auto vec_a_end_addr = vec_a + num_elems_vec_a;
        for(; vec_a <= (vec_a_end_addr - 2);)
        {
            const qint8x8_t a0 = vld1_dup_qs8(vec_a + 0);
            const qint8x8_t a1 = vld1_dup_qs8(vec_a + 1);

            const qint8x8_t b00 = vld1_qs8(matrix_b + 0 + 0 * in_b_stride);
            const qint8x8_t b01 = vld1_qs8(matrix_b + 8 + 0 * in_b_stride);
            const qint8x8_t b02 = vld1_qs8(matrix_b + 16 + 0 * in_b_stride);
            const qint8x8_t b03 = vld1_qs8(matrix_b + 24 + 0 * in_b_stride);
            const qint8x8_t b10 = vld1_qs8(matrix_b + 0 + 1 * in_b_stride);
            const qint8x8_t b11 = vld1_qs8(matrix_b + 8 + 1 * in_b_stride);
            const qint8x8_t b12 = vld1_qs8(matrix_b + 16 + 1 * in_b_stride);
            const qint8x8_t b13 = vld1_qs8(matrix_b + 24 + 1 * in_b_stride);

            // First accumulation
            acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
            acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
            acc02_qs16 = vqmlal_qs8(acc02_qs16, b02, a0, fixed_point_position);
            acc03_qs16 = vqmlal_qs8(acc03_qs16, b03, a0, fixed_point_position);

            // Second accumulation
            acc00_qs16 = vqmlal_qs8(acc00_qs16, b10, a1, fixed_point_position);
            acc01_qs16 = vqmlal_qs8(acc01_qs16, b11, a1, fixed_point_position);
            acc02_qs16 = vqmlal_qs8(acc02_qs16, b12, a1, fixed_point_position);
            acc03_qs16 = vqmlal_qs8(acc03_qs16, b13, a1, fixed_point_position);

            vec_a += 2;
            matrix_b += 2 * in_b_stride;
        }

        for(; vec_a < vec_a_end_addr;)
        {
            const qint8x8_t a0 = vld1_dup_qs8(vec_a);

            const qint8x8_t b00 = vld1_qs8(matrix_b + 0);
            const qint8x8_t b01 = vld1_qs8(matrix_b + 8);
            const qint8x8_t b02 = vld1_qs8(matrix_b + 16);
            const qint8x8_t b03 = vld1_qs8(matrix_b + 24);

            acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
            acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
            acc02_qs16 = vqmlal_qs8(acc02_qs16, b02, a0, fixed_point_position);
            acc03_qs16 = vqmlal_qs8(acc03_qs16, b03, a0, fixed_point_position);

            vec_a += 1;
            matrix_b += in_b_stride;
        }

        // Convert back to qint8x8_t and saturate
        qint8x8_t acc00_qs8 = vqmovn_qs16(acc00_qs16);
        qint8x8_t acc01_qs8 = vqmovn_qs16(acc01_qs16);
        qint8x8_t acc02_qs8 = vqmovn_qs16(acc02_qs16);
        qint8x8_t acc03_qs8 = vqmovn_qs16(acc03_qs16);

        // Multiply by the weight of the matrix product (alpha)
        if(multiply_alpha)
        {
            const qint8x8_t alpha_qs8 = vdup_n_qs8(scvt_qs8_f32(alpha, fixed_point_position));
            acc00_qs8                 = vqmul_qs8(acc00_qs8, alpha_qs8, fixed_point_position);
            acc01_qs8                 = vqmul_qs8(acc01_qs8, alpha_qs8, fixed_point_position);
            acc02_qs8                 = vqmul_qs8(acc02_qs8, alpha_qs8, fixed_point_position);
            acc03_qs8                 = vqmul_qs8(acc03_qs8, alpha_qs8, fixed_point_position);
        }

        const auto mtx_out0 = reinterpret_cast<qint8_t *>(out.ptr());

        // Store 8x4 output elements
        vst1_qs8(mtx_out0 + 0, acc00_qs8);
        vst1_qs8(mtx_out0 + 8, acc01_qs8);
        vst1_qs8(mtx_out0 + 16, acc02_qs8);
        vst1_qs8(mtx_out0 + 24, acc03_qs8);
    },
    ina, inb, out);
}

template <bool multiply_alpha>
void matrix_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
{
    const size_t in_b_stride          = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type());
    const size_t out_stride1          = output->info()->strides_in_bytes()[1] / data_size_from_type(output->info()->data_type());
    const size_t out_stride2          = out_stride1 * 2;
    const size_t out_stride3          = out_stride1 * 3;
    const int    num_elems_matrix_b_x = input1->info()->dimension(0);

    // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
    Window win_a(window);
    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
    win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));

    Window win_b;
    // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
    // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
    if(input1->info()->num_dimensions() >= 3)
    {
        win_b = window;
    }
    // Set step_x and step_y for matrix B. Scale by a factor of 4 the X range as the input transposed matrix A has 4 times less the cols of the output matrix
    // The step along the x direction is 2 times the in_b_stride because for each iteration we compute 2 blocks of size 4x4
    win_b.set(Window::DimX, Window::Dimension(window.x().start() / 4, window.x().end() / 4, 2 * in_b_stride));
    win_b.set(Window::DimY, Window::Dimension(0, 0, 0));

    Iterator ina(input0, win_a);
    Iterator inb(input1, win_b);
    Iterator out(output, window);

    // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with NEGEMMInterleave4x4 and NEGEMMTranspose1xW
    // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration
    // All the values needed for computing a single 4x4 block will be read from consecutive memory positions
    execute_window_loop(window, [&](const Coordinates & id)
    {
        auto mtx_a0 = reinterpret_cast<const float *>(ina.ptr());
        auto mtx_b0 = reinterpret_cast<const float *>(inb.ptr());
        auto mtx_b1 = mtx_b0 + in_b_stride;

        float32x4_t acc00 = vdupq_n_f32(0.f);
        float32x4_t acc10 = vdupq_n_f32(0.f);
        float32x4_t acc20 = vdupq_n_f32(0.f);
        float32x4_t acc30 = vdupq_n_f32(0.f);

        float32x4_t acc01 = vdupq_n_f32(0.f);
        float32x4_t acc11 = vdupq_n_f32(0.f);
        float32x4_t acc21 = vdupq_n_f32(0.f);
        float32x4_t acc31 = vdupq_n_f32(0.f);

#if __arm__
        asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
        asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
        asm volatile("PLD [%0, #128*1]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
#endif

        auto mtx_b0_end_addr = mtx_b0 + num_elems_matrix_b_x;
        for(; mtx_b0 <= (mtx_b0_end_addr - 32);)
        {
            float32x4_t a0 = vld1q_dup_f32(mtx_a0 + 0);
            float32x4_t a1 = vld1q_dup_f32(mtx_a0 + 1);
            float32x4_t a2 = vld1q_dup_f32(mtx_a0 + 2);
            float32x4_t a3 = vld1q_dup_f32(mtx_a0 + 3);

            float32x4_t b00 = vld1q_f32(mtx_b0);
            float32x4_t b10 = vld1q_f32(mtx_b1);
            float32x4_t b01 = vld1q_f32(mtx_b0 + 4);
            float32x4_t b11 = vld1q_f32(mtx_b1 + 4);

#if __arm__
            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
#endif

            // 4x4 block 0
            acc00 = vmlaq_f32(acc00, b00, a0);
            acc10 = vmlaq_f32(acc10, b00, a1);
            acc20 = vmlaq_f32(acc20, b00, a2);
            acc30 = vmlaq_f32(acc30, b00, a3);

            float32x4_t a4 = vld1q_dup_f32(mtx_a0 + 4);
            float32x4_t a5 = vld1q_dup_f32(mtx_a0 + 5);
            float32x4_t a6 = vld1q_dup_f32(mtx_a0 + 6);
            float32x4_t a7 = vld1q_dup_f32(mtx_a0 + 7);

            // 4x4 block 1
            acc01 = vmlaq_f32(acc01, b10, a0);
            acc11 = vmlaq_f32(acc11, b10, a1);
            acc21 = vmlaq_f32(acc21, b10, a2);
            acc31 = vmlaq_f32(acc31, b10, a3);

            // 4x4 block 0
            acc00 = vmlaq_f32(acc00, b01, a4);
            acc10 = vmlaq_f32(acc10, b01, a5);
            acc20 = vmlaq_f32(acc20, b01, a6);
            acc30 = vmlaq_f32(acc30, b01, a7);

            // 4x4 block 1
            acc01 = vmlaq_f32(acc01, b11, a4);
            acc11 = vmlaq_f32(acc11, b11, a5);
            acc21 = vmlaq_f32(acc21, b11, a6);
            acc31 = vmlaq_f32(acc31, b11, a7);

            mtx_a0 += 8;
            mtx_b0 += 8;
            mtx_b1 += 8;

            a0 = vld1q_dup_f32(mtx_a0 + 0);
            a1 = vld1q_dup_f32(mtx_a0 + 1);
            a2 = vld1q_dup_f32(mtx_a0 + 2);
            a3 = vld1q_dup_f32(mtx_a0 + 3);

            b00 = vld1q_f32(mtx_b0);
            b10 = vld1q_f32(mtx_b1);
            b01 = vld1q_f32(mtx_b0 + 4);
            b11 = vld1q_f32(mtx_b1 + 4);

            // 4x4 block 0
            acc00 = vmlaq_f32(acc00, b00, a0);
            acc10 = vmlaq_f32(acc10, b00, a1);
            acc20 = vmlaq_f32(acc20, b00, a2);
            acc30 = vmlaq_f32(acc30, b00, a3);

            a4 = vld1q_dup_f32(mtx_a0 + 4);
            a5 = vld1q_dup_f32(mtx_a0 + 5);
            a6 = vld1q_dup_f32(mtx_a0 + 6);
            a7 = vld1q_dup_f32(mtx_a0 + 7);

            // 4x4 block 1
            acc01 = vmlaq_f32(acc01, b10, a0);
            acc11 = vmlaq_f32(acc11, b10, a1);
            acc21 = vmlaq_f32(acc21, b10, a2);
            acc31 = vmlaq_f32(acc31, b10, a3);

            // 4x4 block 0
            acc00 = vmlaq_f32(acc00, b01, a4);
            acc10 = vmlaq_f32(acc10, b01, a5);
            acc20 = vmlaq_f32(acc20, b01, a6);
            acc30 = vmlaq_f32(acc30, b01, a7);

            // 4x4 block 1
            acc01 = vmlaq_f32(acc01, b11, a4);
            acc11 = vmlaq_f32(acc11, b11, a5);
            acc21 = vmlaq_f32(acc21, b11, a6);
            acc31 = vmlaq_f32(acc31, b11, a7);

            mtx_a0 += 8;
            mtx_b0 += 8;
            mtx_b1 += 8;

            a0  = vld1q_dup_f32(mtx_a0 + 0);
            a1  = vld1q_dup_f32(mtx_a0 + 1);
            a2  = vld1q_dup_f32(mtx_a0 + 2);
            a3  = vld1q_dup_f32(mtx_a0 + 3);
            b00 = vld1q_f32(mtx_b0);
            b10 = vld1q_f32(mtx_b1);
            b01 = vld1q_f32(mtx_b0 + 4);
            b11 = vld1q_f32(mtx_b1 + 4);

#if __arm__
            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
            asm volatile("PLD [%0, #128*4]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
#endif

            // 4x4 block 0
            acc00 = vmlaq_f32(acc00, b00, a0);
            acc10 = vmlaq_f32(acc10, b00, a1);
            acc20 = vmlaq_f32(acc20, b00, a2);
            acc30 = vmlaq_f32(acc30, b00, a3);

            a4 = vld1q_dup_f32(mtx_a0 + 4);
            a5 = vld1q_dup_f32(mtx_a0 + 5);
            a6 = vld1q_dup_f32(mtx_a0 + 6);
            a7 = vld1q_dup_f32(mtx_a0 + 7);

            // 4x4 block 1
            acc01 = vmlaq_f32(acc01, b10, a0);
            acc11 = vmlaq_f32(acc11, b10, a1);
            acc21 = vmlaq_f32(acc21, b10, a2);
            acc31 = vmlaq_f32(acc31, b10, a3);

            // 4x4 block 0
            acc00 = vmlaq_f32(acc00, b01, a4);
            acc10 = vmlaq_f32(acc10, b01, a5);
            acc20 = vmlaq_f32(acc20, b01, a6);
            acc30 = vmlaq_f32(acc30, b01, a7);

            // 4x4 block 1
            acc01 = vmlaq_f32(acc01, b11, a4);
            acc11 = vmlaq_f32(acc11, b11, a5);
            acc21 = vmlaq_f32(acc21, b11, a6);
            acc31 = vmlaq_f32(acc31, b11, a7);

            mtx_a0 += 8;
            mtx_b0 += 8;
            mtx_b1 += 8;

            a0  = vld1q_dup_f32(mtx_a0 + 0);
            a1  = vld1q_dup_f32(mtx_a0 + 1);
            a2  = vld1q_dup_f32(mtx_a0 + 2);
            a3  = vld1q_dup_f32(mtx_a0 + 3);
            b00 = vld1q_f32(mtx_b0);
            b10 = vld1q_f32(mtx_b1);
            b01 = vld1q_f32(mtx_b0 + 4);
            b11 = vld1q_f32(mtx_b1 + 4);

            // 4x4 block 0
            acc00 = vmlaq_f32(acc00, b00, a0);
            acc10 = vmlaq_f32(acc10, b00, a1);
            acc20 = vmlaq_f32(acc20, b00, a2);
            acc30 = vmlaq_f32(acc30, b00, a3);

            a4 = vld1q_dup_f32(mtx_a0 + 4);
            a5 = vld1q_dup_f32(mtx_a0 + 5);
            a6 = vld1q_dup_f32(mtx_a0 + 6);
            a7 = vld1q_dup_f32(mtx_a0 + 7);

            // 4x4 block 1
            acc01 = vmlaq_f32(acc01, b10, a0);
            acc11 = vmlaq_f32(acc11, b10, a1);
            acc21 = vmlaq_f32(acc21, b10, a2);
            acc31 = vmlaq_f32(acc31, b10, a3);

            // 4x4 block 0
            acc00 = vmlaq_f32(acc00, b01, a4);
            acc10 = vmlaq_f32(acc10, b01, a5);
            acc20 = vmlaq_f32(acc20, b01, a6);
            acc30 = vmlaq_f32(acc30, b01, a7);

            // 4x4 block 1
            acc01 = vmlaq_f32(acc01, b11, a4);
            acc11 = vmlaq_f32(acc11, b11, a5);
            acc21 = vmlaq_f32(acc21, b11, a6);
            acc31 = vmlaq_f32(acc31, b11, a7);

            mtx_a0 += 8;
            mtx_b0 += 8;
            mtx_b1 += 8;
        }

        for(; mtx_b0 < mtx_b0_end_addr;)
        {
            float32x4_t a0  = vld1q_dup_f32(mtx_a0 + 0);
            float32x4_t a1  = vld1q_dup_f32(mtx_a0 + 1);
            float32x4_t a2  = vld1q_dup_f32(mtx_a0 + 2);
            float32x4_t a3  = vld1q_dup_f32(mtx_a0 + 3);
            float32x4_t b00 = vld1q_f32(mtx_b0);
            float32x4_t b10 = vld1q_f32(mtx_b1);

#if __arm__
            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
#endif
            // 4x4 block 0
            acc00 = vmlaq_f32(acc00, b00, a0);
            acc10 = vmlaq_f32(acc10, b00, a1);
            acc20 = vmlaq_f32(acc20, b00, a2);
            acc30 = vmlaq_f32(acc30, b00, a3);

            // 4x4 block 1
            acc01 = vmlaq_f32(acc01, b10, a0);
            acc11 = vmlaq_f32(acc11, b10, a1);
            acc21 = vmlaq_f32(acc21, b10, a2);
            acc31 = vmlaq_f32(acc31, b10, a3);

            mtx_a0 += 4;
            mtx_b0 += 4;
            mtx_b1 += 4;
        }

        // Multiply by the weight of matrix product (alpha)
        if(multiply_alpha)
        {
            const float32x4_t alpha_f32 = vdupq_n_f32(alpha);
            acc00                       = vmulq_f32(acc00, alpha_f32);
            acc10                       = vmulq_f32(acc10, alpha_f32);
            acc20                       = vmulq_f32(acc20, alpha_f32);
            acc30                       = vmulq_f32(acc30, alpha_f32);
            acc01                       = vmulq_f32(acc01, alpha_f32);
            acc11                       = vmulq_f32(acc11, alpha_f32);
            acc21                       = vmulq_f32(acc21, alpha_f32);
            acc31                       = vmulq_f32(acc31, alpha_f32);
        }

        const auto mtx_out0 = reinterpret_cast<float *>(out.ptr());
        const auto mtx_out1 = mtx_out0 + 4;

        // Store the 4 blocks
        vst1q_f32(mtx_out0, acc00);
        vst1q_f32(mtx_out1, acc01);
        vst1q_f32(mtx_out0 + out_stride1, acc10);
        vst1q_f32(mtx_out1 + out_stride1, acc11);
        vst1q_f32(mtx_out0 + out_stride2, acc20);
        vst1q_f32(mtx_out1 + out_stride2, acc21);
        vst1q_f32(mtx_out0 + out_stride3, acc30);
        vst1q_f32(mtx_out1 + out_stride3, acc31);
    },
    ina, inb, out);
}

template <bool multiply_alpha>
void matrix_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
{
#ifdef ARM_COMPUTE_ENABLE_FP16
    const size_t in_b_stride = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type());
    const size_t out_stride  = output->info()->strides_in_bytes()[1] / data_size_from_type(output->info()->data_type());

    // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
    Window win_a(window);
    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
    win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));

    Window win_b;
    // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
    // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
    if(input1->info()->num_dimensions() >= 3)
    {
        win_b = window;
    }
    // Set step_x and step_y for matrix B. Scale by a factor of 8 the X range as the input transposed matrix A has 8 times less the cols of the output matrix
    win_b.set(Window::DimX, Window::Dimension(window.x().start() / 8, window.x().end() / 8, in_b_stride));
    win_b.set(Window::DimY, Window::Dimension(0, 1, 0));

    Iterator ina(input0, win_a);
    Iterator inb(input1, win_b);
    Iterator out(output, window);

    // Number of iterations of inner loop. Since 8 is the number of accumulations per loop, num_it = (width_mtx_b / 4) / 8
    const size_t num_it = ((input1->info()->dimension(0)) >> 2) >> 3;

    const float16x8_t alpha_f16 = vdupq_n_f16(alpha);

    execute_window_loop(window, [&](const Coordinates & id)
    {
        const auto   *mtx_a0  = reinterpret_cast<const float16_t *>(ina.ptr());
        const auto   *mtx_b0  = reinterpret_cast<const float16_t *>(inb.ptr());
        auto         *mtx_out = reinterpret_cast<float16_t *>(out.ptr());
        float16x8x4_t c =
        {
            {
                vdupq_n_f16(0.f),
                vdupq_n_f16(0.f),
                vdupq_n_f16(0.f),
                vdupq_n_f16(0.f)
            }
        };

        /*
        This kernel puts the values in a 4x4 block of Matrix A on the same row (Interleaved values)
             |a00 a01 a02 a03 | a04 a05 a06 a07|
             |a10 a11 a12 a13 | a14 a15 a16 a17|
             |a20 a21 a22 a23 | a24 a25 a26 a27| = | a00 a10 a20 a30 || a01 a11 a21 a31 || a02 a12 a22 a32 || a03 a13 a23 a33 | a40 a50 a60 a70 | ...
             |a30 a31 a32 a33 | a34 a35 a36 a37|   | a04 a14 a24 a34 || a05 a15 a25 a35 || a06 a15 a26 a36 || a07 a17 a27 a37 | a44 a54 a64 a74 | ...
             |a40 a41 a42 a43 | a44 a45 a46 a47|
             |a50 a51 a52 a53 | a54 a55 a56 a57|
             |a60 a61 a62 a63 | a64 a65 a66 a67|
             |a70 a71 a72 a73 | a74 a75 a76 a77|

             After this operation, the output matrix will have the following shape: [ height * 4, width / 4 ]

        B Matrix has been transposed as shown below

           |b00 b01 b02 b03 b04 b05 b06 b07|
           |b10 b11 b12 b13 b14 b15 b16 b17|
           |b20 b21 b22 b23 b24 b25 b26 b27|
           |b30 b31 b32 b33 b34 b35 b36 b37|
          ------------------->

           |b00 b01 b02 b03 b04 b05 b06 b07||b10 b11 b12 b13 b14 b15 b16 b17||b20 b21 b22 b23 b24 b25 b26 b27||b30 b31 b32 b33 b34 b35 b36 b37|

            c.val[0][0] = a00*b00 + a01*b10 + a02*b20 + a03*b30
            c.val[0][1] = a00*b01 + a01*b11 + a02*b21 + a03*b31

        The size of the output tensor's XY-plane must be the following shape [ width * 8, height / 8 ]. All other dimensions must have the same size.
        */
        for(size_t k = num_it; k > 0; mtx_a0 += 16, mtx_b0 += 32, --k)
        {
            const float16x8_t p00 = vld1q_f16(mtx_a0);
            const float16x8_t p02 = vld1q_f16(mtx_a0 + 8);
            const float16x8_t q00 = vld1q_f16(mtx_b0);
            const float16x8_t q02 = vld1q_f16(mtx_b0 + 8);
            const float16x8_t q04 = vld1q_f16(mtx_b0 + 16);
            const float16x8_t q06 = vld1q_f16(mtx_b0 + 24);

            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q00, vgetq_lane_f16(p00, 0)));
            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q00, vgetq_lane_f16(p00, 1)));
            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q00, vgetq_lane_f16(p00, 2)));
            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q00, vgetq_lane_f16(p00, 3)));

            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q02, vgetq_lane_f16(p00, 4)));
            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q02, vgetq_lane_f16(p00, 5)));
            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q02, vgetq_lane_f16(p00, 6)));
            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q02, vgetq_lane_f16(p00, 7)));

            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q04, vgetq_lane_f16(p02, 0)));
            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q04, vgetq_lane_f16(p02, 1)));
            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q04, vgetq_lane_f16(p02, 2)));
            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q04, vgetq_lane_f16(p02, 3)));

            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q06, vgetq_lane_f16(p02, 4)));
            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q06, vgetq_lane_f16(p02, 5)));
            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q06, vgetq_lane_f16(p02, 6)));
            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q06, vgetq_lane_f16(p02, 7)));
        }

        if(multiply_alpha)
        {
            c.val[0] = vmulq_f16(c.val[0], alpha_f16);
            c.val[1] = vmulq_f16(c.val[1], alpha_f16);
            c.val[2] = vmulq_f16(c.val[2], alpha_f16);
            c.val[3] = vmulq_f16(c.val[3], alpha_f16);
        }

        vst1q_f16(mtx_out + 0 * out_stride, c.val[0]);
        vst1q_f16(mtx_out + 1 * out_stride, c.val[1]);
        vst1q_f16(mtx_out + 2 * out_stride, c.val[2]);
        vst1q_f16(mtx_out + 3 * out_stride, c.val[3]);
    },
    ina, inb, out);
#else
    ARM_COMPUTE_ERROR("Not implemented");
#endif
}

template <bool multiply_alpha>
void matrix_matrix_multiply_qs8(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
{
    const size_t    in_b_stride          = input1->info()->strides_in_bytes()[1] / data_size_from_type(input1->info()->data_type());
    const size_t    out_stride1          = output->info()->strides_in_bytes()[1] / data_size_from_type(output->info()->data_type());
    const size_t    out_stride2          = out_stride1 * 2;
    const size_t    out_stride3          = out_stride1 * 3;
    const int       num_elems_matrix_b_x = input1->info()->dimension(0);
    const int       fixed_point_position = input0->info()->fixed_point_position();
    const qint8x8_t alpha_qs8            = vdup_n_qs8(scvt_qs8_f32(alpha, fixed_point_position));
    ARM_COMPUTE_UNUSED(alpha_qs8);

    // Set step_x and step_y for matrix A. Scale by a factor of 4 the Y range as the input interleaved matrix A has 4 times less the rows of the output matrix
    Window win_a(window);
    win_a.set(Window::DimX, Window::Dimension(0, 0, 0));
    win_a.set(Window::DimY, Window::Dimension(window.y().start() / 4, std::max(window.y().end() / 4, 1), 1));

    Window win_b;
    // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
    // This scenario can happen when the the matrix multiplication is used to perform a convolution operation
    if(input1->info()->num_dimensions() >= 3)
    {
        win_b = window;
    }
    // Set step_x and step_y for matrix B. Scale by a factor of 16 the X range as the input transposed matrix A has 16 times less the cols of the output matrix
    // The step along the x direction is 2 times the in_b_stride because for each iteration we compute 2 blocks of size 16x4
    win_b.set(Window::DimX, Window::Dimension(window.x().start() / 16, window.x().end() / 16, 2 * in_b_stride));
    win_b.set(Window::DimY, Window::Dimension(0, 0, 0));

    Iterator ina(input0, win_a);
    Iterator inb(input1, win_b);
    Iterator out(output, window);

    // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with NEGEMMInterleave4x4 and NEGEMMTranspose1xW
    // The reshaping of the matrices helps to have a cache friendly implementation and helps to avoid the data re-arrangements needed for computing 16x4 elements per iteration
    // All the values needed for computing a single 32x4 block will be read from consecutive memory positions
    execute_window_loop(window, [&](const Coordinates & id)
    {
        auto mtx_a0 = reinterpret_cast<const qint8_t *>(ina.ptr());
        auto mtx_b0 = reinterpret_cast<const qint8_t *>(inb.ptr());
        auto mtx_b1 = mtx_b0 + in_b_stride;

        qint16x8_t acc00_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc10_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc20_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc30_qs16 = vdupq_n_qs16(0);

        qint16x8_t acc01_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc11_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc21_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc31_qs16 = vdupq_n_qs16(0);

        qint16x8_t acc02_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc12_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc22_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc32_qs16 = vdupq_n_qs16(0);

        qint16x8_t acc03_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc13_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc23_qs16 = vdupq_n_qs16(0);
        qint16x8_t acc33_qs16 = vdupq_n_qs16(0);

        int k = 0;
        // This for loop performs 2 accumulations
        for(; k <= (num_elems_matrix_b_x - 32); k += 32)
        {
            const qint8x8_t a0 = vld1_dup_qs8(mtx_a0 + 0);
            const qint8x8_t a1 = vld1_dup_qs8(mtx_a0 + 1);
            const qint8x8_t a2 = vld1_dup_qs8(mtx_a0 + 2);
            const qint8x8_t a3 = vld1_dup_qs8(mtx_a0 + 3);
            const qint8x8_t a4 = vld1_dup_qs8(mtx_a0 + 4);
            const qint8x8_t a5 = vld1_dup_qs8(mtx_a0 + 5);
            const qint8x8_t a6 = vld1_dup_qs8(mtx_a0 + 6);
            const qint8x8_t a7 = vld1_dup_qs8(mtx_a0 + 7);

            const qint8x8_t b00 = vld1_qs8(mtx_b0 + 0);
            const qint8x8_t b01 = vld1_qs8(mtx_b0 + 8);
            const qint8x8_t b10 = vld1_qs8(mtx_b1 + 0);
            const qint8x8_t b11 = vld1_qs8(mtx_b1 + 8);

            // First accumulation
            acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
            acc10_qs16 = vqmlal_qs8(acc10_qs16, b00, a1, fixed_point_position);
            acc20_qs16 = vqmlal_qs8(acc20_qs16, b00, a2, fixed_point_position);
            acc30_qs16 = vqmlal_qs8(acc30_qs16, b00, a3, fixed_point_position);
            acc02_qs16 = vqmlal_qs8(acc02_qs16, b10, a0, fixed_point_position);
            acc12_qs16 = vqmlal_qs8(acc12_qs16, b10, a1, fixed_point_position);
            acc22_qs16 = vqmlal_qs8(acc22_qs16, b10, a2, fixed_point_position);
            acc32_qs16 = vqmlal_qs8(acc32_qs16, b10, a3, fixed_point_position);

            const qint8x8_t b02 = vld1_qs8(mtx_b0 + 16);
            const qint8x8_t b03 = vld1_qs8(mtx_b0 + 24);
            const qint8x8_t b12 = vld1_qs8(mtx_b1 + 16);
            const qint8x8_t b13 = vld1_qs8(mtx_b1 + 24);

            acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
            acc11_qs16 = vqmlal_qs8(acc11_qs16, b01, a1, fixed_point_position);
            acc21_qs16 = vqmlal_qs8(acc21_qs16, b01, a2, fixed_point_position);
            acc31_qs16 = vqmlal_qs8(acc31_qs16, b01, a3, fixed_point_position);
            acc03_qs16 = vqmlal_qs8(acc03_qs16, b11, a0, fixed_point_position);
            acc13_qs16 = vqmlal_qs8(acc13_qs16, b11, a1, fixed_point_position);
            acc23_qs16 = vqmlal_qs8(acc23_qs16, b11, a2, fixed_point_position);
            acc33_qs16 = vqmlal_qs8(acc33_qs16, b11, a3, fixed_point_position);

#if __arm__
            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_a0)));
            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b0)));
            asm volatile("PLD [%0, #128*2]" ::"r"(reinterpret_cast<const uint8_t *>(mtx_b1)));
#endif

            // Second accumulation
            acc00_qs16 = vqmlal_qs8(acc00_qs16, b02, a4, fixed_point_position);
            acc10_qs16 = vqmlal_qs8(acc10_qs16, b02, a5, fixed_point_position);
            acc20_qs16 = vqmlal_qs8(acc20_qs16, b02, a6, fixed_point_position);
            acc30_qs16 = vqmlal_qs8(acc30_qs16, b02, a7, fixed_point_position);
            acc01_qs16 = vqmlal_qs8(acc01_qs16, b03, a4, fixed_point_position);
            acc11_qs16 = vqmlal_qs8(acc11_qs16, b03, a5, fixed_point_position);
            acc21_qs16 = vqmlal_qs8(acc21_qs16, b03, a6, fixed_point_position);
            acc31_qs16 = vqmlal_qs8(acc31_qs16, b03, a7, fixed_point_position);
            acc02_qs16 = vqmlal_qs8(acc02_qs16, b12, a4, fixed_point_position);
            acc12_qs16 = vqmlal_qs8(acc12_qs16, b12, a5, fixed_point_position);
            acc22_qs16 = vqmlal_qs8(acc22_qs16, b12, a6, fixed_point_position);
            acc32_qs16 = vqmlal_qs8(acc32_qs16, b12, a7, fixed_point_position);
            acc03_qs16 = vqmlal_qs8(acc03_qs16, b13, a4, fixed_point_position);
            acc13_qs16 = vqmlal_qs8(acc13_qs16, b13, a5, fixed_point_position);
            acc23_qs16 = vqmlal_qs8(acc23_qs16, b13, a6, fixed_point_position);
            acc33_qs16 = vqmlal_qs8(acc33_qs16, b13, a7, fixed_point_position);

            mtx_a0 += 8;
            mtx_b0 += 32;
            mtx_b1 += 32;
        }

        // This for loop performs the left over accumulations
        for(; k < num_elems_matrix_b_x; k += 16)
        {
            const qint8x8_t a0 = vld1_dup_qs8(mtx_a0 + 0);
            const qint8x8_t a1 = vld1_dup_qs8(mtx_a0 + 1);
            const qint8x8_t a2 = vld1_dup_qs8(mtx_a0 + 2);
            const qint8x8_t a3 = vld1_dup_qs8(mtx_a0 + 3);

            const qint8x8_t b00 = vld1_qs8(mtx_b0 + 0);
            const qint8x8_t b01 = vld1_qs8(mtx_b0 + 8);
            const qint8x8_t b10 = vld1_qs8(mtx_b1 + 0);
            const qint8x8_t b11 = vld1_qs8(mtx_b1 + 8);

            acc00_qs16 = vqmlal_qs8(acc00_qs16, b00, a0, fixed_point_position);
            acc10_qs16 = vqmlal_qs8(acc10_qs16, b00, a1, fixed_point_position);
            acc20_qs16 = vqmlal_qs8(acc20_qs16, b00, a2, fixed_point_position);
            acc30_qs16 = vqmlal_qs8(acc30_qs16, b00, a3, fixed_point_position);
            acc01_qs16 = vqmlal_qs8(acc01_qs16, b01, a0, fixed_point_position);
            acc11_qs16 = vqmlal_qs8(acc11_qs16, b01, a1, fixed_point_position);
            acc21_qs16 = vqmlal_qs8(acc21_qs16, b01, a2, fixed_point_position);
            acc31_qs16 = vqmlal_qs8(acc31_qs16, b01, a3, fixed_point_position);
            acc02_qs16 = vqmlal_qs8(acc02_qs16, b10, a0, fixed_point_position);
            acc12_qs16 = vqmlal_qs8(acc12_qs16, b10, a1, fixed_point_position);
            acc22_qs16 = vqmlal_qs8(acc22_qs16, b10, a2, fixed_point_position);
            acc32_qs16 = vqmlal_qs8(acc32_qs16, b10, a3, fixed_point_position);
            acc03_qs16 = vqmlal_qs8(acc03_qs16, b11, a0, fixed_point_position);
            acc13_qs16 = vqmlal_qs8(acc13_qs16, b11, a1, fixed_point_position);
            acc23_qs16 = vqmlal_qs8(acc23_qs16, b11, a2, fixed_point_position);
            acc33_qs16 = vqmlal_qs8(acc33_qs16, b11, a3, fixed_point_position);

            mtx_a0 += 4;
            mtx_b0 += 16;
            mtx_b1 += 16;
        }

        // Convert back to qint8x8_t and saturate
        qint8x8_t acc00_qs8 = vqmovn_qs16(acc00_qs16);
        qint8x8_t acc10_qs8 = vqmovn_qs16(acc10_qs16);
        qint8x8_t acc20_qs8 = vqmovn_qs16(acc20_qs16);
        qint8x8_t acc30_qs8 = vqmovn_qs16(acc30_qs16);

        qint8x8_t acc01_qs8 = vqmovn_qs16(acc01_qs16);
        qint8x8_t acc11_qs8 = vqmovn_qs16(acc11_qs16);
        qint8x8_t acc21_qs8 = vqmovn_qs16(acc21_qs16);
        qint8x8_t acc31_qs8 = vqmovn_qs16(acc31_qs16);

        qint8x8_t acc02_qs8 = vqmovn_qs16(acc02_qs16);
        qint8x8_t acc12_qs8 = vqmovn_qs16(acc12_qs16);
        qint8x8_t acc22_qs8 = vqmovn_qs16(acc22_qs16);
        qint8x8_t acc32_qs8 = vqmovn_qs16(acc32_qs16);

        qint8x8_t acc03_qs8 = vqmovn_qs16(acc03_qs16);
        qint8x8_t acc13_qs8 = vqmovn_qs16(acc13_qs16);
        qint8x8_t acc23_qs8 = vqmovn_qs16(acc23_qs16);
        qint8x8_t acc33_qs8 = vqmovn_qs16(acc33_qs16);

        // Multiply by the weight of the matrix product (alpha)
        if(multiply_alpha)
        {
            acc00_qs8 = vqmul_qs8(acc00_qs8, alpha_qs8, fixed_point_position);
            acc10_qs8 = vqmul_qs8(acc10_qs8, alpha_qs8, fixed_point_position);
            acc20_qs8 = vqmul_qs8(acc20_qs8, alpha_qs8, fixed_point_position);
            acc30_qs8 = vqmul_qs8(acc30_qs8, alpha_qs8, fixed_point_position);
            acc01_qs8 = vqmul_qs8(acc01_qs8, alpha_qs8, fixed_point_position);
            acc11_qs8 = vqmul_qs8(acc11_qs8, alpha_qs8, fixed_point_position);
            acc21_qs8 = vqmul_qs8(acc21_qs8, alpha_qs8, fixed_point_position);
            acc31_qs8 = vqmul_qs8(acc31_qs8, alpha_qs8, fixed_point_position);
            acc02_qs8 = vqmul_qs8(acc02_qs8, alpha_qs8, fixed_point_position);
            acc12_qs8 = vqmul_qs8(acc12_qs8, alpha_qs8, fixed_point_position);
            acc22_qs8 = vqmul_qs8(acc22_qs8, alpha_qs8, fixed_point_position);
            acc32_qs8 = vqmul_qs8(acc32_qs8, alpha_qs8, fixed_point_position);
            acc03_qs8 = vqmul_qs8(acc03_qs8, alpha_qs8, fixed_point_position);
            acc13_qs8 = vqmul_qs8(acc13_qs8, alpha_qs8, fixed_point_position);
            acc23_qs8 = vqmul_qs8(acc23_qs8, alpha_qs8, fixed_point_position);
            acc33_qs8 = vqmul_qs8(acc33_qs8, alpha_qs8, fixed_point_position);
        }

        const auto mtx_out0 = reinterpret_cast<qint8_t *>(out.ptr());

        // Store 32x4 output elements
        vst1_qs8(mtx_out0 + 0, acc00_qs8);
        vst1_qs8(mtx_out0 + 8, acc01_qs8);
        vst1_qs8(mtx_out0 + 16, acc02_qs8);
        vst1_qs8(mtx_out0 + 24, acc03_qs8);
        vst1_qs8(mtx_out0 + out_stride1 + 0, acc10_qs8);
        vst1_qs8(mtx_out0 + out_stride1 + 8, acc11_qs8);
        vst1_qs8(mtx_out0 + out_stride1 + 16, acc12_qs8);
        vst1_qs8(mtx_out0 + out_stride1 + 24, acc13_qs8);
        vst1_qs8(mtx_out0 + out_stride2 + 0, acc20_qs8);
        vst1_qs8(mtx_out0 + out_stride2 + 8, acc21_qs8);
        vst1_qs8(mtx_out0 + out_stride2 + 16, acc22_qs8);
        vst1_qs8(mtx_out0 + out_stride2 + 24, acc23_qs8);
        vst1_qs8(mtx_out0 + out_stride3 + 0, acc30_qs8);
        vst1_qs8(mtx_out0 + out_stride3 + 8, acc31_qs8);
        vst1_qs8(mtx_out0 + out_stride3 + 16, acc32_qs8);
        vst1_qs8(mtx_out0 + out_stride3 + 24, acc33_qs8);
    },
    ina, inb, out);
}

} // namespace

NEGEMMMatrixMultiplyKernel::NEGEMMMatrixMultiplyKernel()
    : _input0(nullptr), _input1(nullptr), _output(nullptr), _alpha(1.0f)
{
}

void NEGEMMMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output, float alpha)
{
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32, DataType::QS8);
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32, DataType::QS8);
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32, DataType::QS8);
    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32, DataType::QS8);
    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);

    if(output->info()->dimension(1) == 1)
    {
        ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1));
    }

    _input0 = input0;
    _input1 = input1;
    _output = output;
    _alpha  = alpha;

    unsigned int       num_elems_processed_per_iteration_x = 0;
    const unsigned int num_elems_processed_per_iteration_y = 4;

    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
    if((output->info()->dimension(1) == 1))
    {
        switch(input0->info()->data_type())
        {
            case DataType::F32:
            {
                num_elems_processed_per_iteration_x = 16;
                break;
            }
            case DataType::QS8:
            {
                num_elems_processed_per_iteration_x = 32;
                break;
            }
            default:
            {
                ARM_COMPUTE_ERROR("Data type not supported");
                break;
            }
        }

        // Configure kernel window
        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x));

        AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration_x);

        update_window_and_padding(win,
                                  AccessWindowHorizontal(input0->info(), 0, num_elems_processed_per_iteration_x),
                                  AccessWindowHorizontal(input1->info(), 0, num_elems_processed_per_iteration_x),
                                  output_access);

        Coordinates coord;
        coord.set_num_dimensions(output->info()->num_dimensions());
        output_access.set_valid_region(win, ValidRegion(coord, output->info()->tensor_shape()));

        INEKernel::configure(win);
    }
    else
    {
        switch(input0->info()->data_type())
        {
            case DataType::F32:
            {
                num_elems_processed_per_iteration_x = 8;
                break;
            }
            case DataType::QS8:
            {
                num_elems_processed_per_iteration_x = 32;
                break;
            }
            case DataType::F16:
            {
#ifdef ARM_COMPUTE_ENABLE_FP16
                num_elems_processed_per_iteration_x = 8;
                break;
#endif
            }
            default:
            {
                ARM_COMPUTE_ERROR("Data type not supported");
                break;
            }
        }

        // Configure kernel window
        Window win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));

        AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y);

        update_window_and_padding(win,
                                  AccessWindowRectangle(input0->info(), 0, 0, 4, 1, 1.f, 0.25f),
                                  AccessWindowTranspose(input1->info(), 0, 0, 4, 1, 0.f, 0.25f),
                                  output_access);

        output_access.set_valid_region(win, ValidRegion(Coordinates(0, 0), output->info()->tensor_shape()));

        INEKernel::configure(win);
    }
}

void NEGEMMMatrixMultiplyKernel::run(const Window &window)
{
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);

    bool multiply_alpha = std::abs(1.0f - _alpha) > 0.00001f;

    // Check if the output tensor is a vector and the data type is F32. If so,the kernel runs the vector-matrix multiplication
    if((_output->info()->dimension(1) == 1))
    {
        switch(_input0->info()->data_type())
        {
            case DataType::F32:
            {
                multiply_alpha ? vector_matrix_multiply_f32<true>(_input0, _input1, _output, window, _alpha) :
                vector_matrix_multiply_f32<false>(_input0, _input1, _output, window, _alpha);
                break;
            }
            case DataType::QS8:
            {
                multiply_alpha ? vector_matrix_multiply_qs8<true>(_input0, _input1, _output, window, _alpha) :
                vector_matrix_multiply_qs8<false>(_input0, _input1, _output, window, _alpha);
                break;
            }
            default:
            {
                ARM_COMPUTE_ERROR("Data type not supported");
                break;
            }
        }
    }
    else
    {
        switch(_input0->info()->data_type())
        {
            case DataType::F32:
            {
                multiply_alpha ? matrix_matrix_multiply_f32<true>(_input0, _input1, _output, window, _alpha) :
                matrix_matrix_multiply_f32<false>(_input0, _input1, _output, window, _alpha);
                break;
            }
            case DataType::QS8:
            {
                multiply_alpha ? matrix_matrix_multiply_qs8<true>(_input0, _input1, _output, window, _alpha) :
                matrix_matrix_multiply_qs8<false>(_input0, _input1, _output, window, _alpha);
                break;
            }
            case DataType::F16:
            {
#ifdef ARM_COMPUTE_ENABLE_FP16
                multiply_alpha ? matrix_matrix_multiply_f16<true>(_input0, _input1, _output, window, _alpha) :
                matrix_matrix_multiply_f16<false>(_input0, _input1, _output, window, _alpha);
                break;
#endif
            }
            default:
            {
                ARM_COMPUTE_ERROR("Data type not supported");
                break;
            }
        }
    }
}