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>

12 files changed, 239 insertions, 233 deletions

diff --git a/Android.bp b/Android.bp
index 5b9f4e0276..6250539d02 100644
--- a/Android.bp
+++ b/Android.bp
@@ -166,7 +166,6 @@ cc_library_static {
         "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.cpp",
         "src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.cpp",
         "src/core/NEON/kernels/NEGEMMLowpReductionKernel.cpp",
-        "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp",
         "src/core/NEON/kernels/NEGatherKernel.cpp",
         "src/core/NEON/kernels/NEGenerateProposalsLayerKernel.cpp",
         "src/core/NEON/kernels/NEIm2ColKernel.cpp",
@@ -283,6 +282,7 @@ cc_library_static {
         "src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToInt8ScaleByFixedPointKernel.cpp",
         "src/core/cpu/kernels/CpuGemmLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp",
         "src/core/cpu/kernels/CpuGemmMatrixAdditionKernel.cpp",
+        "src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp",
         "src/core/cpu/kernels/CpuGemmTranspose1xWKernel.cpp",
         "src/core/cpu/kernels/CpuMulKernel.cpp",
         "src/core/cpu/kernels/CpuPermuteKernel.cpp",
diff --git a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
index 22ec9e0fec..e409a61ba1 100644
--- a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h
@@ -79,7 +79,7 @@ private:
 /** Basic function to compute a Fully Connected layer. This function calls the following kernels:
  *  -# @ref NEIm2ColKernel (called when the input comes from a convolutional layer)
  *  -# @ref NETranspose (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once)
- *  -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
+ *  -# @ref NEGEMM or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
  *  -# @ref cpu::kernels::CpuGemmMatrixAdditionKernel or @ref NEGEMMLowpOutputStage (if quantized asymmetric) (if @p biases is not equal to nullptr)
  *
  * @note  The fully connected layer accepts "weights" tensors only with 2 dimensions.
diff --git a/arm_compute/runtime/NEON/functions/NEGEMM.h b/arm_compute/runtime/NEON/functions/NEGEMM.h
index c1ae11bcbf..5daa0406a5 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMM.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMM.h
@@ -42,7 +42,7 @@ namespace arm_compute
  * Else:
  *  -# @ref cpu::kernels::CpuGemmInterleave4x4Kernel (if the output tensor is a matrix)
  *  -# @ref cpu::kernels::CpuGemmTranspose1xWKernel (if the output tensor is a matrix)
- *  -# @ref NEGEMMMatrixMultiplyKernel
+ *  -# @ref cpu::kernels::CpuGemmMatrixMultiplyKernel
  * In both cases:
  *  -# @ref cpu::kernels::CpuGemmMatrixAdditionKernel (if c != nullptr and beta != 0.0 and is not reshaped once)
  * Else:
diff --git a/docs/user_guide/release_version_and_change_log.dox b/docs/user_guide/release_version_and_change_log.dox
index cccf5b9b8b..eb4c280295 100644
--- a/docs/user_guide/release_version_and_change_log.dox
+++ b/docs/user_guide/release_version_and_change_log.dox
@@ -249,7 +249,7 @@ v20.11 Public major release
    - NEConvolutionKernel
    - NEDepthwiseConvolutionLayerNativeKernel
    - @ref NEGEMMLowpMatrixMultiplyKernel
-   - @ref NEGEMMMatrixMultiplyKernel
+   - NEGEMMMatrixMultiplyKernel
    - NEDirectConvolutionLayerOutputStageKernel
    - @ref NEReductionOperationKernel
    - @ref NEGEMMLowpMatrixAReductionKernel
diff --git a/filelist.json b/filelist.json
index b8a69c5f6e..bde361f167 100644
--- a/filelist.json
+++ b/filelist.json
@@ -1164,7 +1164,7 @@
         "files": {
           "kernel": [
             "src/core/cpu/kernels/CpuGemmMatrixAdditionKernel.cpp",
-            "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp",
+            "src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp",
             "src/core/cpu/kernels/CpuGemmTranspose1xWKernel.cpp",
             "src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.cpp"
           ]
diff --git a/src/core/NEON/NEKernels.h b/src/core/NEON/NEKernels.h
index 0f7475c0b5..665c8c7fba 100644
--- a/src/core/NEON/NEKernels.h
+++ b/src/core/NEON/NEKernels.h
@@ -45,7 +45,6 @@
 #include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionKernel.h"
 #include "src/core/NEON/kernels/NEGEMMLowpOffsetContributionOutputStageKernel.h"
 #include "src/core/NEON/kernels/NEGEMMLowpReductionKernel.h"
-#include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
 #include "src/core/NEON/kernels/NEGatherKernel.h"
 #include "src/core/NEON/kernels/NEGenerateProposalsLayerKernel.h"
 #include "src/core/NEON/kernels/NEIm2ColKernel.h"
diff --git a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h
deleted file mode 100644
index 4341ff00df..0000000000
--- a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h
+++ /dev/null
@@ -1,94 +0,0 @@
-/*
- * 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_NEGEMMMATRIXMULTIPLYKERNEL_H
-#define ARM_COMPUTE_NEGEMMMATRIXMULTIPLYKERNEL_H
-
-#include "src/core/NEON/INEKernel.h"
-
-namespace arm_compute
-{
-class ITensor;
-
-/** 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 input0 and @p input1 are both matrices and reshaped respectively with @ref cpu::kernels::CpuGemmInterleave4x4Kernel and @ref cpu::kernels::CpuGemmTranspose1xWKernel
- * @note If the output tensor is a vector and the data type is F32, the implementation assumes that the first input tensor @p input0 is a vector and the second input tensor @p input1 a matrix. The implementation also assumes that both tensors have not been reshaped
- *
- */
-class NEGEMMMatrixMultiplyKernel : public INEKernel
-{
-public:
-    const char *name() const override
-    {
-        return "NEGEMMMatrixMultiplyKernel";
-    }
-    /** Constructor */
-    NEGEMMMatrixMultiplyKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    NEGEMMMatrixMultiplyKernel(const NEGEMMMatrixMultiplyKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    NEGEMMMatrixMultiplyKernel &operator=(const NEGEMMMatrixMultiplyKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    NEGEMMMatrixMultiplyKernel(NEGEMMMatrixMultiplyKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    NEGEMMMatrixMultiplyKernel &operator=(NEGEMMMatrixMultiplyKernel &&) = default;
-    /** Initialise the kernel's input and output.
-     *
-     * @note If the output tensor is a matrix, the input matrices @p input0 and @p input1 should be the output of the kernels: @ref cpu::kernels::CpuGemmInterleave4x4Kernel and @ref cpu::kernels::CpuGemmTranspose1xWKernel
-     *       These two kernels change the layout of the original matrices to be more cache-friendly.
-     *
-     * @param[in]  input0         Input tensor containing the interleaved Matrix A or the vector A. Data types supported: F16/F32
-     * @param[in]  input1         Input tensor containing the transposed Matrix B if the first input tensor A is not a vector.
-     *                            If the output tensor is a vector, input1 must contain the matrix B not reshaped. Data type supported: same as @p input0
-     * @param[out] output         Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0.
-     * @param[in]  alpha          Weight of the matrix product
-     * @param[in]  is_interleaved (Optional) True if input0 and input1 have been reshaped respectively using @ref cpu::kernels::CpuGemmInterleave4x4Kernel and @ref cpu::kernels::CpuGemmTranspose1xWKernel
-     * @param[in]  reshape_info   (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
-     */
-    void configure(const ITensor *input0, const ITensor *input1, ITensor *output, 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 NEGEMMMatrixMultiplyKernel
-     *
-     * @param[in] input0         Input tensor containing the interleaved Matrix A or the vector A. Data types supported: F16/F32
-     * @param[in] input1         Input tensor containing the transposed Matrix B if the first input tensor A is not a vector.
-     *                           If the output tensor is a vector, input1 must contain the matrix B not reshaped. Data type supported: same as @p input0
-     * @param[in] output         Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0.
-     * @param[in] alpha          Weight of the matrix product
-     * @param[in] is_interleaved (Optional) True if input0 and input1 have been reshaped respectively using @ref cpu::kernels::CpuGemmInterleave4x4Kernel and @ref cpu::kernels::CpuGemmTranspose1xWKernel
-     * @param[in] reshape_info   (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info);
-
-    // Inherited methods overridden:
-    void run(const Window &window, const ThreadInfo &info) override;
-
-private:
-    const ITensor *_input0;
-    const ITensor *_input1;
-    ITensor       *_output;
-    float          _alpha;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_NEGEMMMATRIXMULTIPLYKERNEL_H*/
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/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp b/src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp
index b4a3bb5e77..d86ea064de 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp
@@ -21,19 +21,14 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
+#include "src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.h"
 
-#include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensor.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_compute/core/utils/misc/ShapeCalculator.h"
 #include "src/core/CPP/Validate.h"
-#include "src/core/NEON/NEFixedPoint.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/core/utils/helpers/float_ops.h"
@@ -42,14 +37,18 @@
 
 namespace arm_compute
 {
+namespace cpu
+{
+namespace kernels
+{
 namespace
 {
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-void vector_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, const ThreadInfo &info, float alpha)
+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>(output->info()->dimension(0));
-    const auto in_b_stride     = static_cast<int>(input1->info()->strides_in_bytes()[1] / input1->info()->element_size());
-    const auto num_elems_vec_a = static_cast<int>(input0->info()->dimension(0));
+    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;
@@ -68,16 +67,16 @@ void vector_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, IT
     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)
+    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(input0, win_a);
-    Iterator inb(input1, win_b);
-    Iterator out(output, win_out);
+    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));
 
@@ -87,7 +86,7 @@ void vector_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, IT
     {
         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 output.
+        // 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)
@@ -240,11 +239,11 @@ void vector_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, IT
 }
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
 
-void vector_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, const ThreadInfo &info, float alpha)
+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>(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 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;
@@ -263,16 +262,16 @@ void vector_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, IT
     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)
+    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(input0, win_a);
-    Iterator inb(input1, win_b);
-    Iterator out(output, win_out);
+    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));
 
@@ -282,7 +281,7 @@ void vector_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, IT
     {
         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 output.
+        // 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)
@@ -472,17 +471,18 @@ void vector_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, IT
     ina, inb, out);
 }
 
-void matrix_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
+void matrix_matrix_multiply_f32(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
 {
-    const int    out_width            = static_cast<int>(output->info()->dimension(0));
-    const int    out_height           = static_cast<int>(output->info()->dimension(1));
-    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());
+    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 = input1->info()->dimension(0);
+    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 output matrix
+    // 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));
@@ -490,24 +490,24 @@ void matrix_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, IT
     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)
+    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 output matrix
+    // 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(input0, win_a);
-    Iterator inb(input1, win_b);
-    Iterator out(output, window);
+    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 NEGEMMInterleave4x4 and NEGEMMTranspose1xW
+    // 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)
@@ -845,15 +845,16 @@ void matrix_matrix_multiply_f32(const ITensor *input0, const ITensor *input1, IT
 }
 
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-void matrix_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, ITensor *output, const Window &window, float alpha)
+void matrix_matrix_multiply_f16(const ITensor *lhs, const ITensor *rhs, ITensor *dst, const Window &window, const ThreadInfo &info, float alpha)
 {
-    const int    out_width            = static_cast<int>(output->info()->dimension(0));
-    const int    out_height           = static_cast<int>(output->info()->dimension(1));
-    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());
-    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
+    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));
@@ -861,17 +862,17 @@ void matrix_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, IT
     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)
+    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 output matrix
+    // 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(input0, win_a);
-    Iterator inb(input1, win_b);
-    Iterator out(output, window);
+    Iterator ina(lhs, win_a);
+    Iterator inb(rhs, win_b);
+    Iterator out(dst, window);
 
     const bool multiply_alpha = !(helpers::float_ops::is_one(alpha));
 
@@ -903,7 +904,7 @@ void matrix_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, IT
              |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 ]
+             After this operation, the dst matrix will have the following shape: [ height * 4, width / 4 ]
 
         B Matrix has been transposed as shown below
 
@@ -918,7 +919,7 @@ void matrix_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, IT
             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.
+        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;
 
@@ -1022,23 +1023,23 @@ void matrix_matrix_multiply_f16(const ITensor *input0, const ITensor *input1, IT
 }
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
 
-inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
+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(input0);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
+    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(input0->dimension(0) != input1->dimension(1));
+        ARM_COMPUTE_RETURN_ERROR_ON(lhs->dimension(0) != rhs->dimension(1));
 
-        if(output->total_size() != 0)
+        if(dst->total_size() != 0)
         {
-            ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0));
-            ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1));
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+            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
@@ -1050,33 +1051,33 @@ inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *i
         const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
 
         /* Interleave */
-        TensorShape tensor_shape0{ input0->tensor_shape() };
+        TensorShape tensor_shape0{ lhs->tensor_shape() };
         tensor_shape0.set(0, k);
         tensor_shape0.set(1, m);
 
-        const TensorInfo tensor_info0          = input0->clone()->set_tensor_shape(tensor_shape0);
-        const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(misc::shape_calculator::compute_interleaved_shape(tensor_info0, mult_interleave4x4_height));
-        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
+        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{ input1->tensor_shape() };
+            TensorShape tensor_shape1{ rhs->tensor_shape() };
             tensor_shape1.set(0, n);
             tensor_shape1.set(1, k);
 
-            const TensorInfo tensor_info1          = input1->clone()->set_tensor_shape(tensor_shape1);
-            const TensorInfo tensor_info_reshaped1 = input1->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(input1, &tensor_info_reshaped1);
+            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(output->total_size() != 0)
+        if(dst->total_size() != 0)
         {
             if(n != 0)
             {
-                ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast<size_t>(n));
+                ARM_COMPUTE_RETURN_ERROR_ON(dst->dimension(0) != static_cast<size_t>(n));
             }
-            ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast<size_t>(m));
-            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+            ARM_COMPUTE_RETURN_ERROR_ON(dst->dimension(1) != static_cast<size_t>(m));
+            ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
         }
     }
 
@@ -1084,79 +1085,52 @@ inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *i
 }
 } // 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, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
+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(input0, input1, output);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
 
-    // Output tensor auto inizialitation if not yet initialized
-    TensorShape tensor_shape{ input0->info()->tensor_shape() };
-    tensor_shape.set(0, is_interleaved ? reshape_info.n() : input1->info()->dimension(0));
-    tensor_shape.set(1, is_interleaved ? reshape_info.m() : input0->info()->dimension(1));
+    // 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(*output->info(), input0->info()->clone()->set_tensor_shape(tensor_shape));
+    auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(tensor_shape));
 
     // Perform validate step
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), alpha, is_interleaved, reshape_info));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(lhs, rhs, dst, alpha, is_interleaved, reshape_info));
 
-    _input0 = input0;
-    _input1 = input1;
-    _output = output;
-    _alpha  = alpha;
+    _alpha = alpha;
 
     // Configure kernel window
     Window win{};
 
-    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
-    if((output->info()->dimension(1) == 1))
+    // 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 = (input0->info()->data_type() == DataType::F32) ? 16 : 32;
+        const unsigned int num_elems_processed_per_iteration_x = (lhs->data_type() == DataType::F32) ? 16 : 32;
 
-        win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x));
+        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(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+        win = calculate_max_window(*dst, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
     }
 
-    INEKernel::configure(win);
-}
-
-Status NEGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved,
-                                            const GEMMReshapeInfo &reshape_info)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, alpha, is_interleaved, reshape_info));
-
-    return Status{};
-}
-
-void NEGEMMMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &info)
-{
-    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
-    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
-
-    // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication
-    const bool is_output_vector = (_output->info()->dimension(1) == 1);
-    switch(_input0->info()->data_type())
+    switch(lhs->data_type())
     {
         case DataType::F32:
         {
-            is_output_vector ? vector_matrix_multiply_f32(_input0, _input1, _output, window, info, _alpha) :
-            matrix_matrix_multiply_f32(_input0, _input1, _output, window, _alpha);
+            _func = (is_dst_vector) ? vector_matrix_multiply_f32 : matrix_matrix_multiply_f32;
             break;
         }
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
         case DataType::F16:
         {
-            is_output_vector ? vector_matrix_multiply_f16(_input0, _input1, _output, window, info, _alpha) :
-            matrix_matrix_multiply_f16(_input0, _input1, _output, window, _alpha);
+            _func = (is_dst_vector) ? vector_matrix_multiply_f16 : matrix_matrix_multiply_f16;
             break;
         }
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
@@ -1166,5 +1140,35 @@ void NEGEMMMatrixMultiplyKernel::run(const Window &window, const ThreadInfo &inf
             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*/
diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp
index 99a7db72b6..a52ca79504 100644
--- a/src/runtime/NEON/functions/NEGEMM.cpp
+++ b/src/runtime/NEON/functions/NEGEMM.cpp
@@ -34,9 +34,9 @@
 #include "arm_compute/runtime/Tensor.h"
 #include "arm_compute/runtime/TensorAllocator.h"
 #include "src/core/CPP/Validate.h"
-#include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
 #include "src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.h"
 #include "src/core/cpu/kernels/CpuGemmMatrixAdditionKernel.h"
+#include "src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.h"
 #include "src/core/cpu/kernels/CpuGemmTranspose1xWKernel.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/MemoryHelpers.h"
@@ -70,7 +70,7 @@ struct NEGEMM::Impl
 
     std::unique_ptr<cpu::kernels::CpuGemmInterleave4x4Kernel>  interleave_kernel{ nullptr };
     std::unique_ptr<cpu::kernels::CpuGemmTranspose1xWKernel>   transpose_kernel{ nullptr };
-    std::unique_ptr<NEGEMMMatrixMultiplyKernel>                mm_kernel{ nullptr };
+    std::unique_ptr<cpu::kernels::CpuGemmMatrixMultiplyKernel> mm_kernel{ nullptr };
     std::unique_ptr<cpu::CpuGemmAssemblyDispatch>              asm_glue{ nullptr };
     std::unique_ptr<cpu::kernels::CpuGemmMatrixAdditionKernel> ma_kernel{ nullptr };
     std::unique_ptr<cpu::CpuActivation>                        alpha_scale_func{ nullptr };
@@ -167,13 +167,13 @@ void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITe
             _impl->memory_group.manage(&_impl->tmp_d);
         }
 
-        _impl->mm_kernel = std::make_unique<NEGEMMMatrixMultiplyKernel>();
+        _impl->mm_kernel = std::make_unique<cpu::kernels::CpuGemmMatrixMultiplyKernel>();
 
         // Select between GEMV and GEMM
         if(_impl->run_vector_matrix_multiplication)
         {
             // Configure the matrix multiply kernel
-            _impl->mm_kernel->configure(a, b, _impl->gemm_output_to_use, alpha, false);
+            _impl->mm_kernel->configure(a->info(), b->info(), _impl->gemm_output_to_use->info(), alpha, false);
         }
         else
         {
@@ -213,7 +213,7 @@ void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITe
             _impl->transpose_kernel->configure(b->info(), _impl->tmp_b.info());
 
             // Configure matrix multiplication kernel
-            _impl->mm_kernel->configure(&_impl->tmp_a, &_impl->tmp_b, _impl->gemm_output_to_use, alpha, true, GEMMReshapeInfo(m, n, k));
+            _impl->mm_kernel->configure(_impl->tmp_a.info(), _impl->tmp_b.info(), _impl->gemm_output_to_use->info(), alpha, true, GEMMReshapeInfo(m, n, k));
 
             // Allocate once the all configure methods have been called
             _impl->tmp_a.allocator()->allocate();
@@ -342,7 +342,7 @@ Status NEGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITenso
 
         // Validate matrix multiply
         auto_init_if_empty(tmp_output_info, matrix_a_info->clone()->set_tensor_shape(compute_mm_shape(*matrix_a_info, *matrix_b_info, run_interleave_transpose, reshape_info)));
-        ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, &tmp_output_info, alpha, run_interleave_transpose, reshape_info));
+        ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuGemmMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, &tmp_output_info, alpha, run_interleave_transpose, reshape_info));
 
         if(c != nullptr && gemm_info.reshape_b_only_on_first_run())
         {
@@ -383,6 +383,7 @@ void NEGEMM::run()
     }
     else
     {
+        ITensorPack mm_pack{ { ACL_SRC_0, _impl->a }, { ACL_SRC_1, _impl->original_b }, { ACL_DST, _impl->gemm_output_to_use } };
         if(!_impl->run_vector_matrix_multiplication)
         {
             // Run interleave kernel
@@ -395,9 +396,13 @@ void NEGEMM::run()
                 ITensorPack transpose_pack{ { ACL_SRC, _impl->original_b }, { ACL_DST, &_impl->tmp_b } };
                 NEScheduler::get().schedule_op(_impl->transpose_kernel.get(), Window::DimY, _impl->transpose_kernel->window(), transpose_pack);
             }
+
+            // Use reshaped matrices
+            mm_pack.add_const_tensor(ACL_SRC_0, &_impl->tmp_a);
+            mm_pack.add_const_tensor(ACL_SRC_1, &_impl->tmp_b);
         }
 
-        NEScheduler::get().schedule(_impl->mm_kernel.get(), _impl->run_vector_matrix_multiplication ? Window::DimX : Window::DimY);
+        NEScheduler::get().schedule_op(_impl->mm_kernel.get(), _impl->run_vector_matrix_multiplication ? Window::DimX : Window::DimY, _impl->mm_kernel->window(), mm_pack);
 
         // Run bias addition kernel
         if(_impl->run_bias_addition)
diff --git a/tests/validation/NEON/GEMM.cpp b/tests/validation/NEON/GEMM.cpp
index ddd1bca5cc..36c1943f27 100644
--- a/tests/validation/NEON/GEMM.cpp
+++ b/tests/validation/NEON/GEMM.cpp
@@ -25,8 +25,8 @@
 #include "arm_compute/runtime/NEON/functions/NEGEMM.h"
 #include "arm_compute/runtime/Tensor.h"
 #include "arm_compute/runtime/TensorAllocator.h"
-#include "src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
 #include "src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.h"
+#include "src/core/cpu/kernels/CpuGemmMatrixMultiplyKernel.h"
 #include "src/core/cpu/kernels/CpuGemmTranspose1xWKernel.h"
 #include "tests/NEON/Accessor.h"
 #include "tests/NEON/Helper.h"
@@ -113,15 +113,15 @@ bool validate_zero_padding_new(unsigned int dim0_value, unsigned int dim1_value)
 bool validate_gemm_zero_padding(const TensorShape shape0, const TensorShape shape1)
 {
     // Create tensors
-    Tensor in0 = create_tensor<Tensor>(shape0, DataType::F32);
-    Tensor in1 = create_tensor<Tensor>(shape1, DataType::F32);
-    Tensor dst;
+    TensorInfo in0(shape0, 1, DataType::F32);
+    TensorInfo in1(shape1, 1, DataType::F32);
+    TensorInfo dst;
 
     // Validate zero-padding
-    NEGEMMMatrixMultiplyKernel gemm;
+    cpu::kernels::CpuGemmMatrixMultiplyKernel gemm;
     gemm.configure(&in0, &in1, &dst, 1.0, false);
 
-    return in0.info()->padding().empty() && in1.info()->padding().empty() && dst.info()->padding().empty();
+    return in0.padding().empty() && in1.padding().empty() && dst.padding().empty();
 }
 } // namespace