Port NEGEMM to memory injecting interface (Part 2)

- Port NEGEMMMatrixMultiplyKernel to the new API

Partially resolves: COMPMID-4402

Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Change-Id: I52b67055dc24bb3a417d6ec5aeeee86e21b74320
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5873
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>

3 files changed, 1268 insertions, 2 deletions

diff --git a/src/core/cpu/kernels/CpuGemmMatrixAdditionKernel.h b/src/core/cpu/kernels/CpuGemmMatrixAdditionKernel.h
index 216e61b5d5..c8e6fa9589 100644
--- a/src/core/cpu/kernels/CpuGemmMatrixAdditionKernel.h
+++ b/src/core/cpu/kernels/CpuGemmMatrixAdditionKernel.h
@@ -38,7 +38,7 @@ namespace kernels
  * @note [ MTX_OUT = MTX_0 + beta * MTX_1 ] with MTX_0 and MTX_1 of the same size
  *
  * @note This stage is used to finalize the GEMM result and it is computed if and only if beta != 0.0. In case this kernel is used for finalizing GEMM result, we have:
- *        - MTX_0 = A * B * alpha, where MTX_0 is the output of @ref NEGEMMMatrixMultiplyKernel
+ *        - MTX_0 = A * B * alpha, where MTX_0 is the output of @ref CpuGemmMatrixMultiplyKernel
  *        - MTX_1 = C
  */
 class CpuGemmMatrixAdditionKernel : public ICpuKernel
@@ -52,7 +52,7 @@ public:
      * @note The input and output tensor must have the same dimensions
      *
      * @param[in]      src  Input tensor info (Matrix C). Data types supported: F16/F32
-     * @param[in, out] dst  Output tensor info. If this kernel is used to finalize the GEMM result, output contains the result obtained by the kernel @ref NEGEMMMatrixMultiplyKernel. Data type supported: the same as @p src.
+     * @param[in, out] dst  Output tensor info. If this kernel is used to finalize the GEMM result, output contains the result obtained by the kernel @ref CpuGemmMatrixMultiplyKernel. Data type supported: the same as @p src.
      * @param[in]      beta Weight of matrix C
      */
     void configure(const ITensorInfo *src, ITensorInfo *dst, float beta);
diff --git a/src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp b/src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp
new file mode 100644
index 0000000000..d86ea064de
--- /dev/null
+++ b/src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp
@@ -0,0 +1,1174 @@
+/*
+ * Copyright (c) 2017-2021 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 "src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.h"
+
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "src/core/CPP/Validate.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+#include "src/core/utils/helpers/float_ops.h"
+
+#include <arm_neon.h>
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+namespace
+{
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+void vector_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
+{
+    const auto width_matrix_b  = static_cast<int>(dst->info()->dimension(0));
+    const auto in_b_stride     = static_cast<int>(rhs->info()->strides_in_bytes()[1] / rhs->info()->element_size());
+    const auto num_elems_vec_a = static_cast<int>(lhs->info()->dimension(0));
+
+    // The implementation computes 32 elements per iteration
+    const int window_start_x = 32 * info.thread_id;
+    const int window_step_x  = 32 * info.num_threads;
+    const int window_end_x   = ceil_to_multiple(width_matrix_b - window_start_x, window_step_x) + window_start_x;
+    ARM_COMPUTE_ERROR_ON_MSG((window_end_x - window_start_x) % window_step_x, " (window_end_x - window_start_x) must be multiple of window_step_x");
+
+    Window win_out(window);
+    win_out.set(Window::DimX, Window::Dimension(0, 1, 1));
+    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(rhs->info()->num_dimensions() >= 3)
+    {
+        win_b = window;
+    }
+    win_b.set(Window::DimX, Window::Dimension(0, 1, 1));
+    win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    Iterator ina(lhs, win_a);
+    Iterator inb(rhs, win_b);
+    Iterator out(dst, win_out);
+
+    const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
+
+    const float16x8_t alpha_f16 = vdupq_n_f16(alpha);
+
+    execute_window_loop(win_out, [&](const Coordinates &)
+    {
+        int x = window_start_x;
+        // Here we don't check for x lower equal than (window_end_x - window_step_x) because of
+        // window_end_x is computed above which may cause out-of-bound writes to the dst.
+        for(; x < (window_end_x - window_step_x); x += window_step_x)
+        {
+            if(x > width_matrix_b)
+            {
+                return;
+            }
+
+            auto matrix_b = reinterpret_cast<const float16_t *>(inb.ptr()) + x;
+
+            float16x8_t acc0 = vdupq_n_f16(0.f);
+            float16x8_t acc1 = vdupq_n_f16(0.f);
+            float16x8_t acc2 = vdupq_n_f16(0.f);
+            float16x8_t acc3 = vdupq_n_f16(0.f);
+
+            auto             vec_a          = reinterpret_cast<const float16_t *>(ina.ptr());
+            const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a;
+            for(; vec_a <= (vec_a_end_addr - 4);)
+            {
+                const float16x4_t a0l = vld1_f16(vec_a);
+
+                float16x8_t b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
+                float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
+                float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
+                float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
+                float16x8_t b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride);
+                float16x8_t b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride);
+                float16x8_t b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride);
+                float16x8_t b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride);
+
+                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 0));
+                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 0));
+                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 0));
+                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 0));
+                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 1));
+                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 1));
+                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 1));
+                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 1));
+
+                matrix_b += 2 * in_b_stride;
+
+                b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
+                b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
+                b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
+                b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
+                b10 = vld1q_f16(matrix_b + 0 + 1 * in_b_stride);
+                b11 = vld1q_f16(matrix_b + 8 + 1 * in_b_stride);
+                b12 = vld1q_f16(matrix_b + 16 + 1 * in_b_stride);
+                b13 = vld1q_f16(matrix_b + 24 + 1 * in_b_stride);
+
+                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b00, a0l, 2));
+                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b01, a0l, 2));
+                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b02, a0l, 2));
+                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b03, a0l, 2));
+                acc0 = vaddq_f16(acc0, vmulq_lane_f16(b10, a0l, 3));
+                acc1 = vaddq_f16(acc1, vmulq_lane_f16(b11, a0l, 3));
+                acc2 = vaddq_f16(acc2, vmulq_lane_f16(b12, a0l, 3));
+                acc3 = vaddq_f16(acc3, vmulq_lane_f16(b13, a0l, 3));
+
+                vec_a += 4;
+                matrix_b += 2 * in_b_stride;
+            }
+
+            for(; vec_a < vec_a_end_addr; ++vec_a)
+            {
+                const float16_t   a0  = *vec_a;
+                const float16x8_t b00 = vld1q_f16(matrix_b + 0 + 0 * in_b_stride);
+                const float16x8_t b01 = vld1q_f16(matrix_b + 8 + 0 * in_b_stride);
+                const float16x8_t b02 = vld1q_f16(matrix_b + 16 + 0 * in_b_stride);
+                const float16x8_t b03 = vld1q_f16(matrix_b + 24 + 0 * in_b_stride);
+
+                acc0 = vaddq_f16(acc0, vmulq_n_f16(b00, a0));
+                acc1 = vaddq_f16(acc1, vmulq_n_f16(b01, a0));
+                acc2 = vaddq_f16(acc2, vmulq_n_f16(b02, a0));
+                acc3 = vaddq_f16(acc3, vmulq_n_f16(b03, a0));
+
+                matrix_b += in_b_stride;
+            }
+
+            // Multiply by the weight of matrix product (alpha)
+            if(multiply_alpha)
+            {
+                acc0 = vmulq_f16(acc0, alpha_f16);
+                acc1 = vmulq_f16(acc1, alpha_f16);
+                acc2 = vmulq_f16(acc2, alpha_f16);
+                acc3 = vmulq_f16(acc3, alpha_f16);
+            }
+
+            auto vec_out = reinterpret_cast<float16_t *>(out.ptr()) + x;
+
+            vst1q_f16(vec_out + 0, acc0);
+            vst1q_f16(vec_out + 8, acc1);
+            vst1q_f16(vec_out + 16, acc2);
+            vst1q_f16(vec_out + 24, acc3);
+        }
+
+        for(; x < window_end_x; ++x)
+        {
+            if(x > width_matrix_b)
+            {
+                return;
+            }
+
+            auto matrix_b = reinterpret_cast<const float16_t *>(inb.ptr()) + x;
+
+            float16x4_t vacc = vdup_n_f16(0.f);
+
+            auto             vec_a          = reinterpret_cast<const float16_t *>(ina.ptr());
+            const float16_t *vec_a_end_addr = vec_a + num_elems_vec_a;
+            for(; vec_a <= (vec_a_end_addr - 4); vec_a += 4)
+            {
+                const float16x4_t a0l = vld1_f16(vec_a);
+
+                const float16x4_t b_col =
+                {
+                    *(matrix_b + 0 * in_b_stride),
+                    *(matrix_b + 1 * in_b_stride),
+                    *(matrix_b + 2 * in_b_stride),
+                    *(matrix_b + 3 * in_b_stride),
+                };
+
+                vacc = vadd_f16(vacc, vmul_f16(a0l, b_col));
+
+                matrix_b += 4 * in_b_stride;
+            }
+
+            float16_t acc = vget_lane_f16(vacc, 0) + vget_lane_f16(vacc, 1) + vget_lane_f16(vacc, 2) + vget_lane_f16(vacc, 3);
+
+            for(; vec_a < vec_a_end_addr; ++vec_a)
+            {
+                const float16_t a0  = *vec_a;
+                const float16_t b00 = *matrix_b;
+
+                acc += b00 * a0;
+
+                matrix_b += in_b_stride;
+            }
+
+            // Multiply by the weight of matrix product (alpha)
+            if(multiply_alpha)
+            {
+                acc *= static_cast<float16_t>(alpha);
+            }
+
+            auto vec_out = reinterpret_cast<float16_t *>(out.ptr()) + x;
+
+            *(vec_out) = acc;
+        }
+    },
+    ina, inb, out);
+}
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+
+void vector_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
+{
+    const auto width_matrix_b  = static_cast<int>(dst->info()->dimension(0));
+    const auto in_b_stride     = static_cast<int>(rhs->info()->strides_in_bytes()[1] / data_size_from_type(rhs->info()->data_type()));
+    const auto num_elems_vec_a = static_cast<int>(lhs->info()->dimension(0));
+
+    // The implementation computes 16 elements per iteration
+    const int window_start_x = 16 * info.thread_id;
+    const int window_step_x  = 16 * info.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(0, 1, 1));
+    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(rhs->info()->num_dimensions() >= 3)
+    {
+        win_b = window;
+    }
+    win_b.set(Window::DimX, Window::Dimension(0, 1, 1));
+    win_b.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+    Iterator ina(lhs, win_a);
+    Iterator inb(rhs, win_b);
+    Iterator out(dst, win_out);
+
+    const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
+
+    const float32x4_t alpha_f32 = vdupq_n_f32(alpha);
+
+    execute_window_loop(win_out, [&](const Coordinates &)
+    {
+        int x = window_start_x;
+        // Here we don't check for x lower equal than (window_end_x - window_step_x) because of
+        // window_end_x is computed above which may cause out-of-bound writes to the dst.
+        for(; x < (window_end_x - window_step_x); x += window_step_x)
+        {
+            if(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()) + x;
+
+#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 /* __arm__ */
+
+            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 /* __arm__ */
+
+                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; ++vec_a)
+            {
+                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);
+
+                matrix_b += in_b_stride;
+            }
+
+            // Multiply by the weight of matrix product (alpha)
+            if(multiply_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()) + x;
+
+            vst1q_f32(vec_out + 0, acc0);
+            vst1q_f32(vec_out + 4, acc1);
+            vst1q_f32(vec_out + 8, acc2);
+            vst1q_f32(vec_out + 12, acc3);
+        }
+
+        // Left-over loop
+        for(; x < window_end_x; ++x)
+        {
+            if(x > width_matrix_b)
+            {
+                return;
+            }
+
+            float32x4_t vacc = vdupq_n_f32(0.f);
+
+            auto vec_a    = reinterpret_cast<const float *>(ina.ptr());
+            auto matrix_b = reinterpret_cast<const float *>(inb.ptr()) + x;
+
+#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 /* __arm__ */
+
+            auto vec_a_end_addr = vec_a + num_elems_vec_a;
+            for(; vec_a <= (vec_a_end_addr - 4); vec_a += 4)
+            {
+                const float32x4_t a0l = vld1q_f32(vec_a);
+
+                const float32x4_t b_col =
+                {
+                    *(matrix_b + 0 * in_b_stride),
+                    *(matrix_b + 1 * in_b_stride),
+                    *(matrix_b + 2 * in_b_stride),
+                    *(matrix_b + 3 * 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 /* __arm__ */
+
+                vacc = vmlaq_f32(vacc, b_col, a0l);
+
+                matrix_b += 4 * in_b_stride;
+            }
+
+            float acc = vgetq_lane_f32(vacc, 0) + vgetq_lane_f32(vacc, 1) + vgetq_lane_f32(vacc, 2) + vgetq_lane_f32(vacc, 3);
+
+            for(; vec_a < vec_a_end_addr; ++vec_a)
+            {
+                const float a0 = *vec_a;
+
+                const float b00 = *matrix_b;
+
+                acc += b00 * a0;
+
+                matrix_b += in_b_stride;
+            }
+
+            // Multiply by the weight of matrix product (alpha)
+            if(multiply_alpha)
+            {
+                acc *= alpha;
+            }
+
+            const auto vec_out = reinterpret_cast<float *>(out.ptr()) + x;
+
+            *vec_out = acc;
+        }
+    },
+    ina, inb, out);
+}
+
+void matrix_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
+{
+    ARM_COMPUTE_UNUSED(info);
+    const int    out_width            = static_cast<int>(dst->info()->dimension(0));
+    const int    out_height           = static_cast<int>(dst->info()->dimension(1));
+    const size_t in_b_stride          = rhs->info()->strides_in_bytes()[1] / data_size_from_type(rhs->info()->data_type());
+    const size_t out_stride1          = dst->info()->strides_in_bytes()[1] / data_size_from_type(dst->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 = rhs->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 dst 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(rhs->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 dst 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(lhs, win_a);
+    Iterator inb(rhs, win_b);
+    Iterator out(dst, window);
+
+    const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
+
+    const float32x4_t alpha_f32 = vdupq_n_f32(alpha);
+
+    // The implementation assumes that the matrix A and Matrix B have been reshaped respectively with CpuGemmInterleave4x4 and CpuGemmTranspose1xW
+    // 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 /* __arm__ */
+
+        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 /* __arm__ */
+
+            // 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 /* __arm__ */
+
+            // 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 /* __arm__ */
+            // 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)
+        {
+            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;
+
+        if(id.x() < (out_width - 8))
+        {
+            vst1q_f32(mtx_out0, acc00);
+            vst1q_f32(mtx_out1, acc01);
+            if(id.y() + 1 < out_height)
+            {
+                vst1q_f32(mtx_out0 + out_stride1, acc10);
+                vst1q_f32(mtx_out1 + out_stride1, acc11);
+                if(id.y() + 2 < out_height)
+                {
+                    vst1q_f32(mtx_out0 + out_stride2, acc20);
+                    vst1q_f32(mtx_out1 + out_stride2, acc21);
+                    if(id.y() + 3 < out_height)
+                    {
+                        vst1q_f32(mtx_out0 + out_stride3, acc30);
+                        vst1q_f32(mtx_out1 + out_stride3, acc31);
+                    }
+                }
+            }
+        }
+        else if(id.x() < (out_width - 4))
+        {
+            vst1q_f32(mtx_out0, acc00);
+            if(id.y() + 1 < out_height)
+            {
+                vst1q_f32(mtx_out0 + out_stride1, acc10);
+                if(id.y() + 2 < out_height)
+                {
+                    vst1q_f32(mtx_out0 + out_stride2, acc20);
+                    if(id.y() + 3 < out_height)
+                    {
+                        vst1q_f32(mtx_out0 + out_stride3, acc30);
+                    }
+                }
+            }
+            // Left-over columns
+            const int columns_left = out_width - id.x() - 4;
+            for(auto x = 0; x < columns_left; ++x)
+            {
+                *(mtx_out1 + x) = acc01[x];
+                if(id.y() + 1 < out_height)
+                {
+                    *(mtx_out1 + x + out_stride1) = acc11[x];
+                    if(id.y() + 2 < out_height)
+                    {
+                        *(mtx_out1 + x + out_stride2) = acc21[x];
+                        if(id.y() + 3 < out_height)
+                        {
+                            *(mtx_out1 + x + out_stride3) = acc31[x];
+                        }
+                    }
+                }
+            }
+        }
+        else
+        {
+            // Left-over columns
+            const int columns_left = out_width - id.x();
+            for(int x = 0; x < columns_left; ++x)
+            {
+                *(mtx_out0 + x) = acc00[x];
+                if(id.y() + 1 < out_height)
+                {
+                    *(mtx_out0 + x + out_stride1) = acc10[x];
+                    if(id.y() + 2 < out_height)
+                    {
+                        *(mtx_out0 + x + out_stride2) = acc20[x];
+                        if(id.y() + 3 < out_height)
+                        {
+                            *(mtx_out0 + x + out_stride3) = acc30[x];
+                        }
+                    }
+                }
+            }
+        }
+    },
+    ina, inb, out);
+}
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+void matrix_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
+{
+    ARM_COMPUTE_UNUSED(info);
+    const int    out_width            = static_cast<int>(dst->info()->dimension(0));
+    const int    out_height           = static_cast<int>(dst->info()->dimension(1));
+    const size_t in_b_stride          = rhs->info()->strides_in_bytes()[1] / data_size_from_type(rhs->info()->data_type());
+    const size_t out_stride           = dst->info()->strides_in_bytes()[1] / data_size_from_type(dst->info()->data_type());
+    const int    num_elems_matrix_b_x = rhs->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 dst 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(rhs->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 dst 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(lhs, win_a);
+    Iterator inb(rhs, win_b);
+    Iterator out(dst, window);
+
+    const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
+
+    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 dst 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 dst tensor's XY-plane must be the following shape [ width * 8, height / 8 ]. All other dimensions must have the same size.
+        */
+        const float16_t *mtx_b0_end_addr = mtx_b0 + num_elems_matrix_b_x;
+
+        for(; mtx_b0 <= (mtx_b0_end_addr - 32);)
+
+        {
+            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)));
+
+            mtx_a0 += 16;
+            mtx_b0 += 32;
+        }
+
+        for(; mtx_b0 < mtx_b0_end_addr;)
+
+        {
+            const float16x4_t p00 = vld1_f16(mtx_a0);
+            const float16x8_t q00 = vld1q_f16(mtx_b0);
+
+            c.val[0] = vaddq_f16(c.val[0], vmulq_n_f16(q00, vget_lane_f16(p00, 0)));
+            c.val[1] = vaddq_f16(c.val[1], vmulq_n_f16(q00, vget_lane_f16(p00, 1)));
+            c.val[2] = vaddq_f16(c.val[2], vmulq_n_f16(q00, vget_lane_f16(p00, 2)));
+            c.val[3] = vaddq_f16(c.val[3], vmulq_n_f16(q00, vget_lane_f16(p00, 3)));
+
+            mtx_a0 += 4;
+            mtx_b0 += 8;
+        }
+
+        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);
+        }
+
+        if(id.x() < (out_width - 8))
+        {
+            vst1q_f16(mtx_out, c.val[0]);
+            if(id.y() + 1 < out_height)
+            {
+                vst1q_f16(mtx_out + 1 * out_stride, c.val[1]);
+                if(id.y() + 2 < out_height)
+                {
+                    vst1q_f16(mtx_out + 2 * out_stride, c.val[2]);
+                    if(id.y() + 3 < out_height)
+                    {
+                        vst1q_f16(mtx_out + 3 * out_stride, c.val[3]);
+                    }
+                }
+            }
+        }
+        else
+        {
+            // Left-over columns
+            const int columns_left = out_width - id.x();
+            for(int x = 0; x < columns_left; ++x)
+            {
+                *(mtx_out + x) = c.val[0][x];
+                if(id.y() + 1 < out_height)
+                {
+                    *(mtx_out + x + 1 * out_stride) = c.val[1][x];
+                    if(id.y() + 2 < out_height)
+                    {
+                        *(mtx_out + x + 2 * out_stride) = c.val[2][x];
+                        if(id.y() + 3 < out_height)
+                        {
+                            *(mtx_out + x + 3 * out_stride) = c.val[3][x];
+                        }
+                    }
+                }
+            }
+        }
+    },
+    ina, inb, out);
+}
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+
+inline Status validate_arguments(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
+{
+    ARM_COMPUTE_UNUSED(alpha);
+
+    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(lhs);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs, dst);
+
+    if(!is_interleaved)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(lhs->dimension(0) != rhs->dimension(1));
+
+        if(dst->total_size() != 0)
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON(rhs->dimension(0) != dst->dimension(0));
+            ARM_COMPUTE_RETURN_ERROR_ON(lhs->dimension(1) != dst->dimension(1));
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
+        }
+    }
+    else
+    {
+        const int m                         = reshape_info.m();
+        const int n                         = reshape_info.n();
+        const int k                         = reshape_info.k();
+        const int mult_transpose1xW_width   = reshape_info.mult_transpose1xW_width();
+        const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
+
+        /* Interleave */
+        TensorShape tensor_shape0{ lhs->tensor_shape() };
+        tensor_shape0.set(0, k);
+        tensor_shape0.set(1, m);
+
+        const TensorInfo tensor_info0          = lhs->clone()->set_tensor_shape(tensor_shape0);
+        const TensorInfo tensor_info_reshaped0 = lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_interleaved_shape(tensor_info0, mult_interleave4x4_height));
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(lhs, &tensor_info_reshaped0);
+
+        if(n != 0) /* Transpose */
+        {
+            TensorShape tensor_shape1{ rhs->tensor_shape() };
+            tensor_shape1.set(0, n);
+            tensor_shape1.set(1, k);
+
+            const TensorInfo tensor_info1          = rhs->clone()->set_tensor_shape(tensor_shape1);
+            const TensorInfo tensor_info_reshaped1 = rhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transpose1xW_with_element_size_shape(tensor_info1, mult_transpose1xW_width));
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(rhs, &tensor_info_reshaped1);
+        }
+
+        if(dst->total_size() != 0)
+        {
+            if(n != 0)
+            {
+                ARM_COMPUTE_RETURN_ERROR_ON(dst->dimension(0) != static_cast<size_t>(n));
+            }
+            ARM_COMPUTE_RETURN_ERROR_ON(dst->dimension(1) != static_cast<size_t>(m));
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
+        }
+    }
+
+    return Status{};
+}
+} // namespace
+
+void CpuGemmMatrixMultiplyKernel::configure(const ITensorInfo *lhs, const ITensorInfo *rhs, ITensorInfo *dst, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
+
+    // dst tensor auto inizialitation if not yet initialized
+    TensorShape tensor_shape{ lhs->tensor_shape() };
+    tensor_shape.set(0, is_interleaved ? reshape_info.n() : rhs->dimension(0));
+    tensor_shape.set(1, is_interleaved ? reshape_info.m() : lhs->dimension(1));
+
+    auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(tensor_shape));
+
+    // Perform validate step
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(lhs, rhs, dst, alpha, is_interleaved, reshape_info));
+
+    _alpha = alpha;
+
+    // Configure kernel window
+    Window win{};
+
+    // Check if the dst tensor is a vector. If so,the kernel runs the vector-matrix multiplication
+    const bool is_dst_vector = (dst->dimension(1) == 1);
+    if(is_dst_vector)
+    {
+        const unsigned int num_elems_processed_per_iteration_x = (lhs->data_type() == DataType::F32) ? 16 : 32;
+
+        win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x));
+    }
+    else
+    {
+        constexpr unsigned int num_elems_processed_per_iteration_x = 8;
+        constexpr unsigned int num_elems_processed_per_iteration_y = 4;
+
+        win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+    }
+
+    switch(lhs->data_type())
+    {
+        case DataType::F32:
+        {
+            _func = (is_dst_vector) ? vector_matrix_multiply_f32 : matrix_matrix_multiply_f32;
+            break;
+        }
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+        case DataType::F16:
+        {
+            _func = (is_dst_vector) ? vector_matrix_multiply_f16 : matrix_matrix_multiply_f16;
+            break;
+        }
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+        default:
+        {
+            ARM_COMPUTE_ERROR("Data type not supported");
+            break;
+        }
+    }
+    ICPPKernel::configure(win);
+}
+
+Status CpuGemmMatrixMultiplyKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, float alpha, bool is_interleaved,
+                                             const GEMMReshapeInfo &reshape_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(lhs, rhs, dst, alpha, is_interleaved, reshape_info));
+
+    return Status{};
+}
+
+void CpuGemmMatrixMultiplyKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+    ARM_COMPUTE_ERROR_ON(tensors.empty());
+    ARM_COMPUTE_ERROR_ON(_func == nullptr);
+
+    const ITensor *lhs = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+    const ITensor *rhs = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+    ITensor       *dst = tensors.get_tensor(TensorType::ACL_DST);
+
+    (*_func)(lhs, rhs, dst, window, info, _alpha);
+}
+
+const char *CpuGemmMatrixMultiplyKernel::name() const
+{
+    return "CpuGemmMatrixMultiplyKernel";
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
diff --git a/src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.h b/src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.h
new file mode 100644
index 0000000000..bf13342739
--- /dev/null
+++ b/src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.h
@@ -0,0 +1,92 @@
+/*
+ * Copyright (c) 2017-2021 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.
+ */
+#ifndef ARM_COMPUTE_CPU_GEMM_MATRIX_MULTIPLY_KERNEL_H
+#define ARM_COMPUTE_CPU_GEMM_MATRIX_MULTIPLY_KERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+/** Kernel to multiply two input matrices "A" and "B". All elements of the output matrix/vector will be multiplied by alpha after the matrix multiplication
+ *
+ * @note If the output tensor is a matrix, the implementation assumes that the input tensors @p lhs and @p rhs are both matrices and reshaped respectively with @ref CpuGemmInterleave4x4Kernel" and @ref CpuGemmTranspose1xWKernel
+ * @note If the output tensor is a vector and the data type is F32, the implementation assumes that the first input tensor @p lhs is a vector and the second input tensor @p rhs a matrix. The implementation also assumes that both tensors have not been reshaped
+ *
+ */
+class CpuGemmMatrixMultiplyKernel : public ICpuKernel
+{
+public:
+    /** Constructor */
+    CpuGemmMatrixMultiplyKernel() = default;
+    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmMatrixMultiplyKernel);
+    /** Initialise the kernel's input and output.
+     *
+     * @note If the output tensor is a matrix, the input matrices @p lhs and @p rhs should be the output of the kernels: @ref CpuGemmInterleave4x4Kernel and @ref CpuGemmTranspose1xWKernel
+     *       These two kernels change the layout of the original matrices to be more cache-friendly.
+     *
+     * @param[in]  lhs            Left-handside tensor info containing the interleaved Matrix A or the vector A. Data types supported: F16/F32
+     * @param[in]  rhs            Right-handside tensor info containing the transposed Matrix B if the first input tensor A is not a vector.
+     *                            If the output tensor is a vector, rhs must contain the matrix B not reshaped. Data type supported: same as @p lhs
+     * @param[out] dst            Output tensor to store the result of matrix multiplication. Data type supported: same as @p lhs.
+     * @param[in]  alpha          Weight of the matrix product
+     * @param[in]  is_interleaved (Optional) True if lhs and rhs have been reshaped respectively using @ref CpuGemmInterleave4x4Kernel and @ref CpuGemmTranspose1xWKernel
+     * @param[in]  reshape_info   (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how @p lhs and @p rhs have been reshaped
+     */
+    void configure(const ITensorInfo *lhs, const ITensorInfo *rhs, ITensorInfo *dst, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo());
+    /** Static function to check if given info will lead to a valid configuration of @ref CpuGemmMatrixMultiplyKernel
+     *
+     * Similar to @ref CpuGemmMatrixMultiplyKernel::configure()
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info);
+
+    // Inherited methods overridden:
+    void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+    const char *name() const override;
+
+private:
+    /** Common signature for all the matrix multiply functions
+     *
+     * @param[in]  lhs    Left-handside input tensor. Data types supported: F16/F32
+     * @param[in]  rhs    Right-handside input tensor. Data types supported: same as @p lhs
+     * @param[out] dst    The output tensor. Data type supported: same as @p rhs
+     * @param[in]  window Region on which to execute the kernel.
+     * @param[in]  info   Thread info metadata.
+     * @param[in]  alpha  Weight of the matrix product.
+     */
+    using GemmFunctionPtr = void(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha);
+    /** Matrix multiply function to use for the particular tensor types passed to configure() */
+    GemmFunctionPtr *_func{ nullptr };
+    float            _alpha{ 1.f };
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /*ARM_COMPUTE_CPU_GEMM_MATRIX_MULTIPLY_KERNEL_H*/