diff --git a/src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp b/src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp
index 93ae904..03b372e 100644
--- a/src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp
+++ b/src/cpu/kernels/CpuGemmMatrixMultiplyKernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2021 Arm Limited.
+ * Copyright (c) 2017-2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -29,11 +29,10 @@
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "src/core/CPP/Validate.h"
+#include "src/core/common/Registrars.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>
+#include "src/cpu/kernels/gemm_matrix_mul/list.h"
 
 namespace arm_compute
 {
@@ -43,985 +42,25 @@
 {
 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)
+static const std::vector<CpuGemmMatrixMultiplyKernel::GemmMatrixMulKernel> available_kernels =
 {
-    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)
+        "neon_fp32_gemm_matrix_mul",
+        [](const DataTypeISASelectorData & data)
         {
-            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;
-        }
+            return (data.dt == DataType::F32);
+        },
+        REGISTER_FP32_NEON(neon_fp32_gemm_matrix_mul)
     },
-    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)
+        "neon_fp16_gemm_matrix_mul",
+        [](const DataTypeISASelectorData & data)
         {
-            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;
-        }
+            return (data.dt == DataType::F16) && data.isa.fp16;
+        },
+        REGISTER_FP16_NEON(neon_fp16_gemm_matrix_mul)
     },
-    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)
 {
@@ -1083,6 +122,7 @@
 
     return Status{};
 }
+
 } // namespace
 
 void CpuGemmMatrixMultiplyKernel::configure(const ITensorInfo *lhs, const ITensorInfo *rhs, ITensorInfo *dst, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
@@ -1120,26 +160,10 @@
         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;
-        }
-    }
+    const auto uk = CpuGemmMatrixMultiplyKernel::get_implementation(DataTypeISASelectorData{ lhs->data_type(), CPUInfo::get().get_isa() });
+    ARM_COMPUTE_ERROR_ON_NULLPTR(uk);
+    _func = uk->ukernel;
+
     ICPPKernel::configure(win);
 }
 
@@ -1162,13 +186,19 @@
     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 bool is_dst_vector = (dst->info()->dimension(1) == 1);
+    (*_func)(lhs, rhs, dst, window, info, _alpha, is_dst_vector);
 }
 
 const char *CpuGemmMatrixMultiplyKernel::name() const
 {
     return "CpuGemmMatrixMultiplyKernel";
 }
+
+const std::vector<CpuGemmMatrixMultiplyKernel::GemmMatrixMulKernel> &CpuGemmMatrixMultiplyKernel::get_available_kernels()
+{
+    return available_kernels;
+}
 } // namespace kernels
 } // namespace cpu
 } // namespace arm_compute