COMPMID-1406: Refactor gemm_interleaved to use our own types and scheduler

- Ported PrepareB kernel from gemm_interleave
- Ported TransformA feature from gemm_interleave
- Allocate reshaped a and b buffers
- Added memory_manager / memory_group
- MatrixMultiply kernel
- Interleave kernels execution.
- Fixed a few bugs: all nightly Convolution tests passing for threads=1
and threads=4
- Added Doxygen documentations and comments in the code
- Added support for all data types supported

Change-Id: Iffa1c09fda0bb9c61213bb83524d5a48e7ecb03c
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/141281
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

7 files changed, 882 insertions, 18 deletions

diff --git a/src/core/NEON/kernels/assembly/NEGEMMInterleavedMatrixMultiplyWrapper.cpp b/src/core/NEON/kernels/assembly/NEGEMMInterleavedMatrixMultiplyWrapper.cpp
new file mode 100644
index 0000000000..3d42f8a51f
--- /dev/null
+++ b/src/core/NEON/kernels/assembly/NEGEMMInterleavedMatrixMultiplyWrapper.cpp
@@ -0,0 +1,142 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "arm_compute/core/NEON/kernels/assembly/NEGEMMInterleavedMatrixMultiplyWrapper.h"
+
+#include "NEGEMMInterleavedStrategies.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/WindowIterator.h"
+
+namespace arm_compute
+{
+template <typename To, typename Tr, bool use_dot>
+void NEGEMMInterleavedMatrixMultiplyWrapperTemplate<To, Tr, use_dot>::configure(const ITensor *prepared_a, const ITensor *transformed_b, ITensor *tmp_c, ITensor *c, const Window &block_walker,
+                                                                                const BlockSizes &block_sizes, const INEGEMMWrapperKernel::Params &params, bool b_is_pretransposed, float alpha, float beta, unsigned int max_num_threads)
+{
+    using strategy = typename Kernel<To>::strategy;
+
+    _prepared_a         = prepared_a;
+    _transformed_b      = transformed_b;
+    _tmp_c              = tmp_c;
+    _c                  = c;
+    _block_walker       = block_walker;
+    _block_sizes        = block_sizes;
+    _params             = params;
+    _b_is_pretransposed = b_is_pretransposed;
+    _alpha              = alpha;
+    _beta               = beta;
+
+    auto_init_if_empty(*_tmp_c->info(), c->info()->clone()->set_tensor_shape(TensorShape{ _block_sizes.x_block * strategy::out_height(), max_num_threads }));
+}
+
+template <typename To, typename Tr, bool use_dot>
+void NEGEMMInterleavedMatrixMultiplyWrapperTemplate<To, Tr, use_dot>::transform(const MatrixMultiplyWorkload &wl, const ThreadInfo &info, const Window &batch_window, const Coordinates &start_offset,
+                                                                                const Coordinates &end_offset)
+{
+    using strategy = typename Kernel<To>::strategy;
+
+    strategy           strat(info.cpu_info);
+    TensorAccessor<To> prepared_a(*_prepared_a);
+    TensorAccessor<To> transformed_b(*_transformed_b);
+    TensorAccessor<Tr> c(*_c);
+    TensorAccessor<Tr> tmp_c(*_tmp_c);
+
+    int  prev_batch      = -1;
+    To *a_ptr           = nullptr;
+    auto window_iterator = arm_compute::create_window_iterator(batch_window, start_offset, end_offset, [&](const Coordinates & id)
+    {
+        const unsigned int y     = id.x();
+        const unsigned int batch = id.y();
+        const unsigned int ymax  = std::min(_params.M, y + strategy::out_height());
+
+        // If it's the first block of a new batch then reset the pointer to A.
+        if(prev_batch != static_cast<int>(batch))
+        {
+            const unsigned int first_m = id.x();
+            a_ptr                      = prepared_a(0, first_m, batch);
+            prev_batch                 = batch;
+        }
+
+        // Call matrix multiply assembly routine to process the block:
+        strat.kernel(a_ptr, transformed_b(wl._offset_transformed_b), tmp_c(0, info.thread_id), 1, wl._bblocks, wl._kern_k);
+        a_ptr += strategy::out_height() * wl._kern_k;
+
+        // Merge the result with the other blocks' results:
+        strat.transforms.Merge(c(0, 0, batch, wl._multi), tmp_c(0, info.thread_id), c.stride(1), y, ymax, wl._x0, wl._xmax, _alpha, (wl._k0 == 0 ? _beta : static_cast<Tr>(1)));
+    });
+    auto on_new_row_size = [&](unsigned int start, unsigned int end)
+    {
+        //Nothing to do
+    };
+    window_iterator.iterate_2D(on_new_row_size);
+}
+
+template <typename To, typename Tr, bool use_dot>
+void NEGEMMInterleavedMatrixMultiplyWrapperTemplate<To, Tr, use_dot>::create_workloads(std::vector<MatrixMultiplyWorkload> &workloads)
+{
+    using strategy = typename Kernel<To>::strategy;
+
+    unsigned int offset_transformed_b = 0;
+    execute_window_loop(_block_walker, [&](const Coordinates & id)
+    {
+        const unsigned int x0    = id.x();
+        const unsigned int k0    = id.y();
+        const unsigned int multi = id.z();
+
+        const unsigned int xmax = std::min(x0 + _block_walker.x().step(), _params.N);
+        const unsigned int kmax = std::min(k0 + _block_walker.y().step(), _params.K);
+
+        // Figure out how many "K" the kernel will actually process.
+        const int kern_k  = ceil_to_multiple(kmax - k0, strategy::k_unroll());
+        const int bblocks = DIV_CEIL(xmax - x0, strategy::out_width());
+
+        workloads.push_back(MatrixMultiplyWorkload(offset_transformed_b, x0, xmax, k0, kmax, multi, kern_k, bblocks));
+
+        if(_b_is_pretransposed)
+        {
+            offset_transformed_b += bblocks * strategy::out_width() * kern_k;
+        }
+        else
+        {
+            ARM_COMPUTE_ERROR("Not supported");
+        }
+    });
+}
+
+//TODO: regroup somewhere ?
+template class NEGEMMInterleavedMatrixMultiplyWrapperTemplate<float, float>;
+#ifdef __aarch64__
+template class NEGEMMInterleavedMatrixMultiplyWrapperTemplate<uint8_t, uint32_t>;
+template class NEGEMMInterleavedMatrixMultiplyWrapperTemplate<int8_t, int32_t>;
+template class NEGEMMInterleavedMatrixMultiplyWrapperTemplate<uint8_t, uint32_t, true>;
+template class NEGEMMInterleavedMatrixMultiplyWrapperTemplate<int8_t, int32_t, true>;
+#endif /* __aarch64__ */
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+template class NEGEMMInterleavedMatrixMultiplyWrapperTemplate<float16_t, float16_t>;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+} // namespace arm_compute
diff --git a/src/core/NEON/kernels/assembly/NEGEMMInterleavedPrepareBWrapperKernel.cpp b/src/core/NEON/kernels/assembly/NEGEMMInterleavedPrepareBWrapperKernel.cpp
new file mode 100644
index 0000000000..f33a14f2af
--- /dev/null
+++ b/src/core/NEON/kernels/assembly/NEGEMMInterleavedPrepareBWrapperKernel.cpp
@@ -0,0 +1,170 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "arm_compute/core/NEON/kernels/assembly/NEGEMMInterleavedPrepareBWrapperKernel.h"
+
+#include "NEGEMMInterleavedStrategies.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+
+namespace arm_compute
+{
+namespace
+{
+// Call the lambda function for each workload generated by the passed window.
+template <typename To, bool use_dot, typename Lambda>
+void for_each_element_in_window(const Window &window, const ITensor *b, ITensor *transformed_b, unsigned int N, unsigned int K, Lambda &&lambda)
+{
+    using strategy = typename Kernel<To, use_dot>::strategy;
+
+    unsigned int offset_transformed_b = transformed_b->info()->offset_first_element_in_bytes();
+    execute_window_loop(window, [&](const Coordinates & coordinates)
+    {
+        const unsigned int x0    = coordinates.x();
+        const unsigned int k0    = coordinates.y();
+        const unsigned int multi = coordinates.z();
+
+        const unsigned int offset_b = b->info()->offset_element_in_bytes(Coordinates(0, 0, multi));
+        const unsigned int xmax     = std::min(x0 + window.x().step(), N);
+        const unsigned int kmax     = std::min(k0 + window.y().step(), K);
+
+        /* Figure out the size of each block. */
+        unsigned int x_size = (xmax - x0);
+        unsigned int k_size = (kmax - k0);
+
+        /* Round sizes up as needed. */
+        x_size = ceil_to_multiple(x_size, strategy::out_width());
+        k_size = ceil_to_multiple(k_size, strategy::k_unroll());
+
+        lambda(PrepareBWorkload(offset_b, offset_transformed_b, x0, xmax, k0, kmax));
+
+        //Each workload represents one block:
+        offset_transformed_b += (x_size * k_size * sizeof(To));
+    });
+}
+
+// Calculate the size of transformed_b:
+template <typename To, bool use_dot>
+unsigned int get_B_pretransposed_array_size(unsigned int N, unsigned int K, const BlockSizes &bs)
+{
+    using strategy = typename Kernel<To, use_dot>::strategy;
+
+    // How many full blocks do N / K contain ?
+    size_t num_full_k = K / bs.k_block;
+    size_t num_full_x = N / bs.x_block;
+
+    ARM_COMPUTE_ERROR_ON(bs.x_block % strategy::out_width() != 0);
+    ARM_COMPUTE_ERROR_ON(bs.k_block % strategy::k_unroll() != 0);
+
+    size_t normal_x_size = bs.x_block;
+    size_t normal_k_size = bs.k_block;
+
+    // Round up the leftovers to be a multiple of the strategy processing size:
+    size_t left_over_x_size = ceil_to_multiple(N % bs.x_block, strategy::out_width());
+    size_t left_over_k_size = ceil_to_multiple(K % bs.k_block, strategy::k_unroll());
+
+    // Calculate the total size of the buffer:
+    size_t total = num_full_k * normal_k_size * (num_full_x * normal_x_size + left_over_x_size);
+    total += left_over_k_size * (left_over_x_size + num_full_x * normal_x_size);
+    total *= sizeof(To);
+    return total;
+}
+
+} // namespace
+
+template <typename To, bool use_dot>
+BlockSizes NEGEMMInterleavedPrepareBWrapperKernelTemplate<To, use_dot>::block_sizes() const
+{
+    return _block_sizes;
+}
+
+template <typename To, bool use_dot>
+void NEGEMMInterleavedPrepareBWrapperKernelTemplate<To, use_dot>::configure(const ITensor *b, ITensor *transformed_b, bool transpose_b, const CPUInfo &ci, const INEGEMMWrapperKernel::Params &params)
+{
+    using strategy = typename Kernel<To, use_dot>::strategy;
+
+    const unsigned int multis = b->info()->tensor_shape().z();
+    _Nsize                    = b->info()->tensor_shape().x();
+    _Ksize                    = b->info()->tensor_shape().y();
+    _b                        = b;
+    _transformed_b            = transformed_b;
+    _transpose_b              = transpose_b;
+
+    _block_sizes = calculate_block_sizes<strategy>(ci, params.M, params.N, params.K);
+
+    auto_init_if_empty(*transformed_b->info(), b->info()->clone()->set_tensor_shape(TensorShape{ get_B_pretransposed_array_size<To, use_dot>(_Nsize, _Ksize, _block_sizes) }));
+
+    Window window;
+    window.set(Window::DimX, Window::Dimension(0, ceil_to_multiple(_Nsize, _block_sizes.x_block), _block_sizes.x_block));
+    window.set(Window::DimY, Window::Dimension(0, ceil_to_multiple(_Ksize, _block_sizes.k_block), _block_sizes.k_block));
+    window.set(Window::DimZ, Window::Dimension(0, multis));
+
+    INEKernel::configure(window);
+}
+
+template <typename To, bool use_dot>
+void NEGEMMInterleavedPrepareBWrapperKernelTemplate<To, use_dot>::transform(const PrepareBWorkload &wl, const ThreadInfo &info)
+{
+    using strategy = typename Kernel<To, use_dot>::strategy;
+
+    strategy strat(info.cpu_info);
+    strat.transforms.PrepareB(reinterpret_cast<To *>(_transformed_b->buffer() + wl._offset_transformed_b),
+                              reinterpret_cast<To *>(_b->buffer() + wl._offset_b),
+                              _b->info()->strides_in_bytes().y() / sizeof(To),
+                              wl._x0, wl._xmax, wl._k0, wl._kmax, _transpose_b);
+}
+
+template <typename To, bool use_dot>
+void NEGEMMInterleavedPrepareBWrapperKernelTemplate<To, use_dot>::create_workloads(std::vector<PrepareBWorkload> &workloads)
+{
+    for_each_element_in_window<To, use_dot>(window(), _b, _transformed_b, _Nsize, _Ksize, [&workloads](PrepareBWorkload && wl)
+    {
+        workloads.push_back(std::move(wl));
+    });
+}
+
+template <typename To, bool use_dot>
+void NEGEMMInterleavedPrepareBWrapperKernelTemplate<To, use_dot>::run(const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(window, INEKernel::window());
+    for_each_element_in_window<To, use_dot>(window, _b, _transformed_b, _Nsize, _Ksize, [&](PrepareBWorkload && wl)
+    {
+        this->transform(wl, info);
+    });
+}
+
+template class NEGEMMInterleavedPrepareBWrapperKernelTemplate<float>;
+#ifdef __aarch64__
+template class NEGEMMInterleavedPrepareBWrapperKernelTemplate<uint8_t>;
+template class NEGEMMInterleavedPrepareBWrapperKernelTemplate<int8_t>;
+template class NEGEMMInterleavedPrepareBWrapperKernelTemplate<uint8_t, true>;
+template class NEGEMMInterleavedPrepareBWrapperKernelTemplate<int8_t, true>;
+#endif /* __aarch64__ */
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+template class NEGEMMInterleavedPrepareBWrapperKernelTemplate<float16_t>;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+} // namespace arm_compute
diff --git a/src/core/NEON/kernels/assembly/NEGEMMInterleavedStrategies.h b/src/core/NEON/kernels/assembly/NEGEMMInterleavedStrategies.h
new file mode 100644
index 0000000000..26a8ade461
--- /dev/null
+++ b/src/core/NEON/kernels/assembly/NEGEMMInterleavedStrategies.h
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2018 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_NEGEMMINTERLEAVEDSTRATEGIES_H__
+#define __ARM_COMPUTE_NEGEMMINTERLEAVEDSTRATEGIES_H__
+
+#include "../arm_gemm/utils.hpp"
+#include "arm_gemm.hpp"
+
+#include "../arm_gemm/mergeresults.hpp"
+#include "../arm_gemm/transform.hpp"
+
+#include "../arm_gemm/kernels/a32_sgemm_8x6.hpp"
+#include "../arm_gemm/kernels/a64_gemm_s8_12x8.hpp"
+#include "../arm_gemm/kernels/a64_gemm_s8_4x4.hpp"
+#include "../arm_gemm/kernels/a64_gemm_u8_12x8.hpp"
+#include "../arm_gemm/kernels/a64_gemm_u8_4x4.hpp"
+#include "../arm_gemm/kernels/a64_hgemm_24x8.hpp"
+#include "../arm_gemm/kernels/a64_sgemm_12x8.hpp"
+
+namespace arm_compute
+{
+namespace
+{
+template <typename To, bool use_dot = false>
+struct Kernel
+{
+};
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+template <>
+struct Kernel<float16_t, false>
+{
+    using strategy = arm_gemm::hgemm_24x8;
+};
+#endif /*__ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+#ifdef __aarch64__
+template <>
+struct Kernel<float, false>
+{
+    using strategy = arm_gemm::sgemm_12x8;
+};
+template <>
+struct Kernel<int8_t, false>
+{
+    using strategy = arm_gemm::gemm_s8_4x4;
+};
+template <>
+struct Kernel<uint8_t, false>
+{
+    using strategy = arm_gemm::gemm_u8_4x4;
+};
+
+//Use different strategies for 8bit dot product:
+template <>
+struct Kernel<int8_t, true>
+{
+    using strategy = arm_gemm::gemm_s8_12x8;
+};
+template <>
+struct Kernel<uint8_t, true>
+{
+    using strategy = arm_gemm::gemm_u8_12x8;
+};
+#else
+template <>
+struct Kernel<float, false>
+{
+    using strategy = arm_gemm::sgemm_8x6;
+};
+#endif /* __aarch64__ */
+
+} // namespace
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_NEGEMMINTERLEAVEDSTRATEGIES_H__ */
diff --git a/src/core/NEON/kernels/assembly/NEGEMMInterleavedTransformAWrapper.cpp b/src/core/NEON/kernels/assembly/NEGEMMInterleavedTransformAWrapper.cpp
new file mode 100644
index 0000000000..3b80a1f940
--- /dev/null
+++ b/src/core/NEON/kernels/assembly/NEGEMMInterleavedTransformAWrapper.cpp
@@ -0,0 +1,118 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "arm_compute/core/NEON/kernels/assembly/NEGEMMInterleavedTransformAWrapper.h"
+
+#include "NEGEMMInterleavedStrategies.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/WindowIterator.h"
+
+#include "utils/TypePrinter.h"
+
+namespace arm_compute
+{
+template <typename To, bool use_dot>
+void NEGEMMInterleavedTransformAWrapperTemplate<To, use_dot>::configure(const ITensor *a, ITensor *transformed_a, bool transpose_a, const Window &block_walker,
+                                                                        const INEGEMMWrapperKernel::Params &params)
+{
+    _a              = a;
+    _transformed_a  = transformed_a;
+    _transpose_a    = transpose_a;
+    _Ksize          = params.K;
+    _Msize          = params.M;
+    _k_multi_window = block_walker.shift_dimensions(1); // block_walker contains (M,K,Multi) --> shift by 1 to get rid of the "M" dimension
+}
+
+template <typename To, bool use_dot>
+void NEGEMMInterleavedTransformAWrapperTemplate<To, use_dot>::transform(const TransformAWorkload &wl, const ThreadInfo &info, const Window &batch_window, const Coordinates &start_offset,
+                                                                        const Coordinates &end_offset)
+{
+    using strategy = typename Kernel<To, use_dot>::strategy;
+
+    strategy           strat(info.cpu_info);
+    TensorAccessor<To> a(*_a);
+    TensorAccessor<To> transformed_a(*_transformed_a);
+
+    if(_a->info()->data_layout() == DataLayout::NHWC)
+    {
+        // In the case of NHWC we want to interpret the output shape as 3D. Thus, the batch stride for A is
+        // the relevant multiple of the row stride.
+        const size_t nhwc_batch_stride = _a->info()->strides_in_bytes().y() * _Msize;
+        a.set_stride(2, nhwc_batch_stride);
+    }
+
+    unsigned int last_m = 0;
+    //TODO: Create a new iterate_1D( DimY);
+    int  last_y          = -1;
+    auto window_iterator = arm_compute::create_window_iterator(batch_window, start_offset, end_offset, [&](const Coordinates & id)
+    {
+        if(id.y() != last_y)
+        {
+            last_y               = id.y();
+            unsigned int batch   = id.y();
+            unsigned int first_m = id.x();
+
+            if(first_m >= last_m)
+                return;
+
+            strat.transforms.PrepareA(transformed_a(0, first_m, batch),
+                                      a(0, 0, batch, wl._multi),
+                                      a.stride(1), first_m, last_m, wl._k0, wl._kmax, _transpose_a);
+        }
+    });
+    auto on_new_row_size = [&](unsigned int start, unsigned int end)
+    {
+        last_m = std::min(end, _Msize);
+    };
+    window_iterator.iterate_2D(on_new_row_size);
+}
+
+template <typename To, bool use_dot>
+void NEGEMMInterleavedTransformAWrapperTemplate<To, use_dot>::create_workloads(std::vector<TransformAWorkload> &workloads)
+{
+    execute_window_loop(_k_multi_window, [&](const Coordinates & id)
+    {
+        const unsigned int k0    = id.x();
+        const unsigned int multi = id.y();
+        const unsigned int kmax  = std::min(k0 + _k_multi_window.x().step(), _Ksize);
+
+        workloads.push_back(TransformAWorkload(k0, kmax, multi));
+    });
+}
+
+template class NEGEMMInterleavedTransformAWrapperTemplate<float>;
+#ifdef __aarch64__
+template class NEGEMMInterleavedTransformAWrapperTemplate<uint8_t>;
+template class NEGEMMInterleavedTransformAWrapperTemplate<int8_t>;
+template class NEGEMMInterleavedTransformAWrapperTemplate<uint8_t, true>;
+template class NEGEMMInterleavedTransformAWrapperTemplate<int8_t, true>;
+#endif /* __aarch64__ */
+
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+template class NEGEMMInterleavedTransformAWrapperTemplate<float16_t>;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+} // namespace arm_compute
diff --git a/src/core/NEON/kernels/assembly/NEGEMMNativeWrapperKernel.cpp b/src/core/NEON/kernels/assembly/NEGEMMNativeWrapperKernel.cpp
index ea6a06cada..e452dfbcf2 100644
--- a/src/core/NEON/kernels/assembly/NEGEMMNativeWrapperKernel.cpp
+++ b/src/core/NEON/kernels/assembly/NEGEMMNativeWrapperKernel.cpp
@@ -42,6 +42,8 @@
 
 namespace arm_compute
 {
+namespace
+{
 template <typename To, typename Tr>
 struct Kernel
 {
@@ -55,6 +57,8 @@ struct Kernel<float, float>
 };
 #endif /* __aarch64__ */
 
+} // namespace
+
 template <typename To, typename Tr>
 Window NEGEMMNativeWrapperKernel<To, Tr>::configure_internal(float alpha, float beta)
 {
diff --git a/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.cpp b/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.cpp
index f17da7d2e4..8ba620fe51 100644
--- a/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.cpp
+++ b/src/runtime/NEON/functions/NEGEMMAssemblyDispatch.cpp
@@ -24,9 +24,13 @@
 #include "arm_compute/runtime/NEON/functions/NEGEMMAssemblyDispatch.h"
 
 #include "arm_compute/core/CPP/Validate.h"
+#include "arm_compute/core/NEON/kernels/assembly/NEGEMMInterleavedMatrixMultiplyWrapper.h"
+#include "arm_compute/core/NEON/kernels/assembly/NEGEMMInterleavedPrepareBWrapperKernel.h"
+#include "arm_compute/core/NEON/kernels/assembly/NEGEMMInterleavedTransformAWrapper.h"
 #include "arm_compute/core/NEON/kernels/assembly/NEGEMMNativeWrapperKernel.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "arm_compute/runtime/NEON/functions/NESimpleAssemblyFunction.h"
+#include "arm_compute/runtime/NEON/functions/assembly/NEGEMMInterleavedWrapper.h"
 
 #include <arm_neon.h>
 
@@ -34,8 +38,31 @@ namespace arm_compute
 {
 namespace
 {
+std::unique_ptr<IFunction> create_function_all_types(arm_gemm::GemmMethod method, const ITensor *a, const ITensor *b, ITensor *d, float alpha, float beta, bool pretranspose_hint,
+                                                     std::shared_ptr<IMemoryManager> memory_manager)
+
+{
+    //Note: It's safe to not check for FP16 support because this was already checked in NEGEMMAssemblyDispatch::configure()
+    switch(method)
+    {
+        case arm_gemm::GemmMethod::GEMM_INTERLEAVED:
+        {
+            if(!pretranspose_hint)
+            {
+                return nullptr;
+            }
+            auto function = support::cpp14::make_unique<NEGEMMInterleavedWrapper>(memory_manager);
+            function->configure(a, b, d, alpha, beta, pretranspose_hint);
+            return std::move(function);
+        }
+        default:
+            return nullptr;
+    }
+}
+
 template <typename TypeInput, typename TypeOutput>
-std::unique_ptr<IFunction> create_function(arm_gemm::GemmMethod method, const ITensor *a, const ITensor *b, ITensor *d, float alpha, float beta, bool pretranspose_hint)
+std::unique_ptr<IFunction> create_function(arm_gemm::GemmMethod method, const ITensor *a, const ITensor *b, ITensor *d, float alpha, float beta, bool pretranspose_hint,
+                                           std::shared_ptr<IMemoryManager> memory_manager)
 {
     ARM_COMPUTE_UNUSED(method);
     ARM_COMPUTE_UNUSED(a);
@@ -44,21 +71,63 @@ std::unique_ptr<IFunction> create_function(arm_gemm::GemmMethod method, const IT
     ARM_COMPUTE_UNUSED(alpha);
     ARM_COMPUTE_UNUSED(beta);
     ARM_COMPUTE_UNUSED(pretranspose_hint);
+    ARM_COMPUTE_UNUSED(memory_manager);
     return nullptr;
 }
+
+#ifdef __aarch64__
 template <>
-std::unique_ptr<IFunction> create_function<float, float>(arm_gemm::GemmMethod method, const ITensor *a, const ITensor *b, ITensor *d, float alpha, float beta, bool pretranspose_hint)
+std::unique_ptr<IFunction> create_function<int8_t, int32_t>(arm_gemm::GemmMethod method, const ITensor *a, const ITensor *b, ITensor *d, float alpha, float beta, bool pretranspose_hint,
+                                                            std::shared_ptr<IMemoryManager> memory_manager)
+{
+    switch(method)
+    {
+        case arm_gemm::GemmMethod::GEMM_INTERLEAVED_DOT:
+        {
+            if(!pretranspose_hint)
+            {
+                return nullptr;
+            }
+            auto function = support::cpp14::make_unique<NEGEMMInterleavedWrapper>(memory_manager);
+            function->configure(a, b, d, alpha, beta, pretranspose_hint, true /* use_dot */);
+            return std::move(function);
+        }
+        default:
+            return nullptr;
+    }
+    return nullptr;
+}
+
+template <>
+std::unique_ptr<IFunction> create_function<uint8_t, uint32_t>(arm_gemm::GemmMethod method, const ITensor *a, const ITensor *b, ITensor *d, float alpha, float beta, bool pretranspose_hint,
+                                                              std::shared_ptr<IMemoryManager> memory_manager)
+{
+    switch(method)
+    {
+        case arm_gemm::GemmMethod::GEMM_INTERLEAVED_DOT:
+        {
+            if(!pretranspose_hint)
+            {
+                return nullptr;
+            }
+            auto function = support::cpp14::make_unique<NEGEMMInterleavedWrapper>(memory_manager);
+            function->configure(a, b, d, alpha, beta, pretranspose_hint, true /* use_dot */);
+            return std::move(function);
+        }
+        default:
+            return nullptr;
+    }
+    return nullptr;
+}
+
+template <>
+std::unique_ptr<IFunction> create_function<float, float>(arm_gemm::GemmMethod method, const ITensor *a, const ITensor *b, ITensor *d, float alpha, float beta, bool pretranspose_hint,
+                                                         std::shared_ptr<IMemoryManager> memory_manager)
 {
-    ARM_COMPUTE_UNUSED(method);
-    ARM_COMPUTE_UNUSED(a);
-    ARM_COMPUTE_UNUSED(b);
-    ARM_COMPUTE_UNUSED(d);
-    ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_UNUSED(beta);
     ARM_COMPUTE_UNUSED(pretranspose_hint);
+    ARM_COMPUTE_UNUSED(memory_manager);
     switch(method)
     {
-#ifdef __aarch64__
         case arm_gemm::GemmMethod::GEMM_NATIVE:
         {
             auto kernel = support::cpp14::make_unique<NEGEMMNativeWrapperKernel<float, float>>();
@@ -67,11 +136,11 @@ std::unique_ptr<IFunction> create_function<float, float>(arm_gemm::GemmMethod me
             function->configure(std::move(kernel));
             return std::move(function);
         }
-#endif /* __aarch64__ */
         default:
             return nullptr;
     }
 }
+#endif /* __aarch64__ */
 
 /** Fallback in case ACL doesn't have a function */
 template <typename TypeInput, typename TypeOutput>
@@ -173,11 +242,11 @@ void Fallback<TypeInput, TypeOutput>::prepare()
         // Pretranspose B if required
         if(_gemm_kernel_asm->B_pretranspose_required())
         {
+            ARM_COMPUTE_ERROR_ON(_pretranspose.buffer() == nullptr);
             const int  ldb            = _b->info()->strides_in_bytes().y() / sizeof(TypeInput);
             const auto in1_ptr        = reinterpret_cast<const TypeInput *>(_b->buffer() + _b->info()->offset_first_element_in_bytes());
             const int  multi_stride_b = _b->info()->strides_in_bytes().z() / sizeof(TypeInput);
 
-            ARM_COMPUTE_ERROR_ON(_pretranspose.buffer() == nullptr);
             _gemm_kernel_asm->pretranspose_B_array(_pretranspose.buffer(), in1_ptr, ldb, multi_stride_b);
             _b->mark_as_unused();
         }
@@ -260,7 +329,7 @@ void Fallback<TypeInput, TypeOutput>::run()
 
 template <typename TypeInput, typename TypeOutput>
 void create_function_or_arm_gemm(std::unique_ptr<IFunction> &acl_function, std::unique_ptr<NEGEMMAssemblyDispatch::IFallback> &arm_gemm, MemoryGroup &memory_group, const ITensor *a, const ITensor *b,
-                                 ITensor *d, float alpha, float beta, bool pretranspose_hint)
+                                 ITensor *d, float alpha, float beta, bool pretranspose_hint, std::shared_ptr<IMemoryManager> memory_manager)
 {
     INEGEMMWrapperKernel::Params p           = INEGEMMWrapperKernel::extract_parameters(a, b, d);
     const CPUInfo               &ci          = NEScheduler::get().cpu_info();
@@ -269,7 +338,13 @@ void create_function_or_arm_gemm(std::unique_ptr<IFunction> &acl_function, std::
     arm_gemm::GemmArgs<TypeOutput> args(&ci, p.M, p.N, p.K, p.batches, p.multis, false, false, alpha, beta, num_threads, pretranspose_hint);
 
     //Try to create an ACL function:
-    acl_function = create_function<TypeInput, TypeOutput>(arm_gemm::get_gemm_method<TypeInput, TypeOutput>(args), a, b, d, alpha, beta, pretranspose_hint);
+    acl_function = create_function_all_types(arm_gemm::get_gemm_method<TypeInput, TypeOutput>(args), a, b, d, alpha, beta, pretranspose_hint, memory_manager);
+    // If the type agnostic factory failed to create an ACL function, try the specialised one:
+    if(acl_function == nullptr)
+    {
+        acl_function = create_function<TypeInput, TypeOutput>(arm_gemm::get_gemm_method<TypeInput, TypeOutput>(args), a, b, d, alpha, beta, pretranspose_hint, memory_manager);
+    }
+    //If we still don't have an ACL function:
     if(acl_function == nullptr)
     {
         //Fallback onto arm_gemm function if ACL doesn't support this method.
@@ -282,7 +357,7 @@ void create_function_or_arm_gemm(std::unique_ptr<IFunction> &acl_function, std::
 } //namespace
 
 NEGEMMAssemblyDispatch::NEGEMMAssemblyDispatch(std::shared_ptr<IMemoryManager> memory_manager)
-    : _function(nullptr), _arm_gemm(nullptr), _memory_group(std::move(memory_manager))
+    : _function(nullptr), _arm_gemm(nullptr), _memory_group(memory_manager), _memory_manager(memory_manager)
 {
 }
 
@@ -321,20 +396,20 @@ void NEGEMMAssemblyDispatch::configure(const ITensor *a, const ITensor *b, ITens
     switch(a->info()->data_type())
     {
         case DataType::F32:
-            create_function_or_arm_gemm<float, float>(_function, _arm_gemm, _memory_group, a, b, d, alpha, beta, pretranspose_hint);
+            create_function_or_arm_gemm<float, float>(_function, _arm_gemm, _memory_group, a, b, d, alpha, beta, pretranspose_hint, _memory_manager);
             break;
 #ifdef __aarch64__
         case DataType::U8:
         case DataType::QASYMM8:
-            create_function_or_arm_gemm<uint8_t, uint32_t>(_function, _arm_gemm, _memory_group, a, b, d, alpha, beta, pretranspose_hint);
+            create_function_or_arm_gemm<uint8_t, uint32_t>(_function, _arm_gemm, _memory_group, a, b, d, alpha, beta, pretranspose_hint, _memory_manager);
             break;
         case DataType::S8:
-            create_function_or_arm_gemm<int8_t, int32_t>(_function, _arm_gemm, _memory_group, a, b, d, alpha, beta, pretranspose_hint);
+            create_function_or_arm_gemm<int8_t, int32_t>(_function, _arm_gemm, _memory_group, a, b, d, alpha, beta, pretranspose_hint, _memory_manager);
             break;
 #endif /* __aarch64__ */
 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
         case DataType::F16:
-            create_function_or_arm_gemm<float16_t, float16_t>(_function, _arm_gemm, _memory_group, a, b, d, alpha, beta, pretranspose_hint);
+            create_function_or_arm_gemm<float16_t, float16_t>(_function, _arm_gemm, _memory_group, a, b, d, alpha, beta, pretranspose_hint, _memory_manager);
             break;
 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
         default:
diff --git a/src/runtime/NEON/functions/assembly/NEGEMMInterleavedWrapper.cpp b/src/runtime/NEON/functions/assembly/NEGEMMInterleavedWrapper.cpp
new file mode 100644
index 0000000000..434723ca1a
--- /dev/null
+++ b/src/runtime/NEON/functions/assembly/NEGEMMInterleavedWrapper.cpp
@@ -0,0 +1,260 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+
+#include "arm_compute/runtime/NEON/functions/assembly/NEGEMMInterleavedWrapper.h"
+
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/NEON/kernels/assembly/NEGEMMInterleavedMatrixMultiplyWrapper.h"
+#include "arm_compute/core/NEON/kernels/assembly/NEGEMMInterleavedPrepareBWrapperKernel.h"
+#include "arm_compute/core/NEON/kernels/assembly/NEGEMMInterleavedTransformAWrapper.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+
+namespace arm_compute
+{
+NEGEMMInterleavedWrapper::NEGEMMInterleavedWrapper(std::shared_ptr<IMemoryManager> memory_manager)
+    : _memory_group(std::move(memory_manager))
+{
+}
+void NEGEMMInterleavedWrapper::run()
+{
+    prepare();
+
+    _memory_group.acquire();
+    NEScheduler::get().run_workloads(_workloads);
+    _memory_group.release();
+}
+
+void NEGEMMInterleavedWrapper::prepare()
+{
+    if(!_is_prepared)
+    {
+        if(_pretranspose_b)
+        {
+            NEScheduler::get().schedule(_prepare_b.get(), Window::DimX);
+            _b->mark_as_unused();
+        }
+        else
+        {
+            _prepare_b->create_workloads(_b_workloads);
+        }
+        _transform_a->create_workloads(_a_workloads);
+        _matrix_multiply->create_workloads(_mm_workloads);
+
+        //Maximum number of workloads to create:
+        const unsigned int num_threads    = NEScheduler::get().num_threads();
+        const unsigned int max_iterations = num_threads == 1 ? 1 : num_threads * 4;
+        //Maximum number of iterations the parameters allow:
+        const unsigned int num_iterations = _batch_window.num_iterations_total();
+        // Keep the smallest of the two:
+        const unsigned int num_windows  = std::min(num_iterations, max_iterations);
+        const TensorShape  window_shape = _batch_window.shape();
+
+        // Create a 1D window to dynamically split the batch window:
+        Window win_1D;
+        win_1D.set(0, Window::Dimension(0, num_iterations));
+
+        // Create one workload for each sub-window:
+        for(unsigned int w = 0; w < num_windows; w++)
+        {
+            Window             win          = win_1D.split_window(0, w, num_windows);
+            const Coordinates  start_offset = index2coords(window_shape, win.x().start());
+            const Coordinates  end_offset   = index2coords(window_shape, win.x().end() - 1);
+            const unsigned int num_x_blocks = _block_walker.num_iterations(Window::DimX);
+
+            auto workload = [start_offset, end_offset, num_x_blocks, this](const ThreadInfo & info)
+            {
+                //For each block of rows in "M"
+                auto workload_mm = this->_mm_workloads.begin();
+                for(auto workload_a = this->_a_workloads.begin(); workload_a != this->_a_workloads.end(); workload_a++)
+                {
+                    // Transform one k_block from A:
+                    this->_transform_a->transform(*workload_a, info, this->_batch_window, start_offset, end_offset);
+                    // Then perform the matrix multiplication for each x block along N:
+                    for(unsigned int i = 0; i < num_x_blocks; i++)
+                    {
+                        ARM_COMPUTE_ERROR_ON(workload_mm == this->_mm_workloads.end());
+                        this->_matrix_multiply->transform(*workload_mm++, info, this->_batch_window, start_offset, end_offset);
+                    }
+                }
+            };
+            _workloads.push_back(workload);
+        }
+
+        _is_prepared = true;
+    }
+}
+
+namespace
+{
+// Factory to instantiate NEGEMMInterleavedPrepareBWrapperKernel:
+template <typename InputType, bool use_dot = false>
+std::unique_ptr<NEGEMMInterleavedPrepareBWrapperKernel> instantiate_prepareB(const ITensor *b, ITensor *transformed_b, const INEGEMMWrapperKernel::Params &params)
+{
+    auto prepare_b = support::cpp14::make_unique<NEGEMMInterleavedPrepareBWrapperKernelTemplate<InputType, use_dot>>();
+    prepare_b->configure(b, transformed_b, false, NEScheduler::get().cpu_info(), params);
+    return std::move(prepare_b);
+}
+
+// Factory to instantiate NEGEMMInterleavedTransformAWrapperTemplate:
+template <typename InputType, bool use_dot = false>
+std::unique_ptr<NEGEMMInterleavedTransformAWrapper> instantiate_transformA(const ITensor *a, ITensor *transformed_a, const Window &block_walker, const INEGEMMWrapperKernel::Params &params)
+{
+    auto transform_a = support::cpp14::make_unique<NEGEMMInterleavedTransformAWrapperTemplate<InputType, use_dot>>();
+    transform_a->configure(a, transformed_a, false, block_walker, params);
+    return std::move(transform_a);
+}
+
+// Factory to instantiate NEGEMMInterleavedTransformAWrapperTemplate:
+template <typename InputType, typename OutputType, bool use_dot = false>
+std::unique_ptr<NEGEMMInterleavedMatrixMultiplyWrapper> instantiate_matrix_multiply(const ITensor *transformed_a, const ITensor *transformed_b, ITensor *tmp_c, ITensor *c, const Window &block_walker,
+                                                                                    const BlockSizes &block_sizes, const INEGEMMWrapperKernel::Params &params, bool pretranspose_b, float alpha, float beta)
+{
+    auto matrix_multiply = support::cpp14::make_unique<NEGEMMInterleavedMatrixMultiplyWrapperTemplate<InputType, OutputType, use_dot>>();
+    matrix_multiply->configure(transformed_a, transformed_b, tmp_c, c, block_walker, block_sizes, params, pretranspose_b, alpha, beta, NEScheduler::get().num_threads());
+    return std::move(matrix_multiply);
+}
+} // namespace
+
+void NEGEMMInterleavedWrapper::configure(const ITensor *a, const ITensor *b, ITensor *c, float alpha, float beta, bool pretranspose_b, bool use_dot)
+{
+    _params         = INEGEMMWrapperKernel::extract_parameters(a, b, c);
+    _a              = a;
+    _b              = b;
+    _c              = c;
+    _pretranspose_b = pretranspose_b;
+
+    DataType input_type = a->info()->data_type();
+
+    // Forcing 128-byte alignment (required by 32-bit kernels)
+    const unsigned int alignment = 128;
+    _transformed_b.allocator()->init(TensorInfo{}, alignment);
+    _tmp_c.allocator()->init(TensorInfo{}, alignment);
+    if(!_pretranspose_b)
+    {
+        // If B is transposed at every iteration then transformed_B can be managed:
+        _memory_group.manage(&_transformed_b);
+    }
+    switch(input_type)
+    {
+        case DataType::F32:
+            _prepare_b = instantiate_prepareB<float>(_b, &_transformed_b, _params);
+            break;
+#ifdef __aarch64__
+        case DataType::U8:
+        case DataType::QASYMM8:
+            if(use_dot)
+            {
+                _prepare_b = instantiate_prepareB<uint8_t, true>(_b, &_transformed_b, _params);
+            }
+            else
+            {
+                _prepare_b = instantiate_prepareB<uint8_t, false>(_b, &_transformed_b, _params);
+            }
+            break;
+        case DataType::S8:
+            if(use_dot)
+            {
+                _prepare_b = instantiate_prepareB<int8_t, true>(_b, &_transformed_b, _params);
+            }
+            else
+            {
+                _prepare_b = instantiate_prepareB<int8_t, false>(_b, &_transformed_b, _params);
+            }
+            break;
+#endif /* __aarch64__ */
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+        case DataType::F16:
+            _prepare_b = instantiate_prepareB<__fp16>(_b, &_transformed_b, _params);
+            break;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+        default:
+            ARM_COMPUTE_ERROR("DataType not supported");
+            break;
+    }
+    ARM_COMPUTE_ERROR_ON(_prepare_b == nullptr);
+
+    _block_sizes = _prepare_b->block_sizes();
+
+    _block_walker.set(Window::DimX, Window::Dimension(0, ceil_to_multiple(_params.N, _block_sizes.x_block), _block_sizes.x_block));
+    _block_walker.set(Window::DimY, Window::Dimension(0, ceil_to_multiple(_params.K, _block_sizes.k_block), _block_sizes.k_block));
+    _block_walker.set(Window::DimZ, Window::Dimension(0, _params.multis));
+
+    _batch_window.set(Window::DimX, Window::Dimension(0, ceil_to_multiple(_block_sizes.m_round, _block_sizes.strategy_out_height), _block_sizes.strategy_out_height));
+    _batch_window.set(Window::DimY, Window::Dimension(0, _params.batches));
+
+    _transformed_a.allocator()->init(TensorInfo(TensorShape{ _block_sizes.k_block, _block_sizes.m_round, _params.batches }, 1, input_type), alignment);
+    _memory_group.manage(&_transformed_a);
+    _memory_group.manage(&_tmp_c);
+
+    switch(input_type)
+    {
+        case DataType::F32:
+            _transform_a     = instantiate_transformA<float>(_a, &_transformed_a, _block_walker, _params);
+            _matrix_multiply = instantiate_matrix_multiply<float, float>(&_transformed_a, &_transformed_b, &_tmp_c, c, _block_walker, _block_sizes, _params, pretranspose_b, alpha, beta);
+            break;
+#ifdef __aarch64__
+        case DataType::U8:
+        case DataType::QASYMM8:
+            if(use_dot)
+            {
+                _transform_a     = instantiate_transformA<uint8_t, true>(_a, &_transformed_a, _block_walker, _params);
+                _matrix_multiply = instantiate_matrix_multiply<uint8_t, uint32_t, true>(&_transformed_a, &_transformed_b, &_tmp_c, c, _block_walker, _block_sizes, _params, pretranspose_b, alpha, beta);
+            }
+            else
+            {
+                _transform_a     = instantiate_transformA<uint8_t, false>(_a, &_transformed_a, _block_walker, _params);
+                _matrix_multiply = instantiate_matrix_multiply<uint8_t, uint32_t, false>(&_transformed_a, &_transformed_b, &_tmp_c, c, _block_walker, _block_sizes, _params, pretranspose_b, alpha, beta);
+            }
+            break;
+        case DataType::S8:
+            if(use_dot)
+            {
+                _transform_a     = instantiate_transformA<int8_t, true>(_a, &_transformed_a, _block_walker, _params);
+                _matrix_multiply = instantiate_matrix_multiply<int8_t, int32_t, true>(&_transformed_a, &_transformed_b, &_tmp_c, c, _block_walker, _block_sizes, _params, pretranspose_b, alpha, beta);
+            }
+            else
+            {
+                _transform_a     = instantiate_transformA<int8_t, false>(_a, &_transformed_a, _block_walker, _params);
+                _matrix_multiply = instantiate_matrix_multiply<int8_t, int32_t, false>(&_transformed_a, &_transformed_b, &_tmp_c, c, _block_walker, _block_sizes, _params, pretranspose_b, alpha, beta);
+            }
+            break;
+#endif /* __aarch64__ */
+#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
+        case DataType::F16:
+            _transform_a     = instantiate_transformA<__fp16>(_a, &_transformed_a, _block_walker, _params);
+            _matrix_multiply = instantiate_matrix_multiply<__fp16, __fp16>(&_transformed_a, &_transformed_b, &_tmp_c, c, _block_walker, _block_sizes, _params, pretranspose_b, alpha, beta);
+            break;
+            break;
+#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
+        default:
+            break;
+    }
+    ARM_COMPUTE_ERROR_ON(_transform_a == nullptr);
+    ARM_COMPUTE_ERROR_ON(_matrix_multiply == nullptr);
+    _transformed_a.allocator()->allocate();
+    _tmp_c.allocator()->allocate();
+    _transformed_b.allocator()->allocate();
+}
+} // namespace arm_compute