diff --git a/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp b/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp
new file mode 100644
index 0000000..651ce43
--- /dev/null
+++ b/src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.cpp
@@ -0,0 +1,717 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/runtime/cpu/operators/CpuGemmLowpMatrixMultiplyCore.h"
+
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/KernelDescriptors.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "arm_compute/runtime/TensorAllocator.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/MemoryHelpers.h"
+
+#include "src/core/cpu/kernels/CpuConvertQuantizedSignednessKernel.h"
+#include "src/core/cpu/kernels/CpuGemmInterleave4x4Kernel.h"
+#include "src/core/cpu/kernels/CpuGemmLowpMatrixMultiplyKernel.h"
+#include "src/core/cpu/kernels/CpuGemmLowpMatrixReductionKernel.h"
+#include "src/core/cpu/kernels/CpuGemmLowpOffsetContributionKernel.h"
+#include "src/core/cpu/kernels/CpuGemmLowpOffsetContributionOutputStageKernel.h"
+#include "src/core/cpu/kernels/CpuGemmTranspose1xWKernel.h"
+#include "src/runtime/cpu/operators/CpuActivation.h"
+#include "src/runtime/cpu/operators/internal/CpuGemmAssemblyDispatch.h"
+#include "src/runtime/cpu/utils/CpuAuxTensorHandler.h"
+
+using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::experimental;
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace
+{
+cpu::AsmGemmInfo init_assembly_metadata(const GEMMInfo &info)
+{
+    cpu::AsmGemmInfo asm_info;
+    asm_info.method                  = cpu::AsmConvMethod::Im2Col;
+    asm_info.reinterpret_input_as_3d = info.reinterpret_input_as_3d();
+    asm_info.depth_output_gemm3d     = info.depth_output_gemm3d();
+    asm_info.activation_info         = info.activation_info();
+    asm_info.output_stage            = info.gemmlowp_output_stage();
+
+    return asm_info;
+}
+} // namespace
+
+CpuGemmLowpMatrixMultiplyCore::CpuGemmLowpMatrixMultiplyCore()
+    : _asm_glue(std::make_unique<CpuGemmAssemblyDispatch>()),
+      _mm_kernel(),
+      _mtx_a_reshape_kernel(),
+      _mtx_b_reshape_kernel(),
+      _mtx_a_reduction_kernel(),
+      _mtx_b_reduction_kernel(),
+      _offset_contribution_kernel(),
+      _offset_contribution_output_stage_kernel(),
+      _activation_func(),
+      _convert_to_signed_asymm(),
+      _convert_from_signed_asymm(),
+      _vector_sum_col(),
+      _vector_sum_row(),
+      _tmp_a(),
+      _tmp_b(),
+      _mm_result_s32(),
+      _signed_a(),
+      _signed_output(),
+      _a_offset(0),
+      _b_offset(0),
+      _run_vector_matrix_multiplication(false),
+      _assembly_path(false),
+      _fused_assembly_path(false),
+      _reshape_b_only_on_first_run(false),
+      _is_prepared(false),
+      _fuse_output_stage(false),
+      _run_activation(false),
+      _flip_signedness(false),
+      _gemm_info(),
+      _aux_mem(Count)
+{
+}
+CpuGemmLowpMatrixMultiplyCore::~CpuGemmLowpMatrixMultiplyCore() = default;
+
+void CpuGemmLowpMatrixMultiplyCore::configure(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *dst, const GEMMInfo &gemm_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, dst);
+    ARM_COMPUTE_ERROR_THROW_ON(CpuGemmLowpMatrixMultiplyCore::validate(a, b, c, dst, gemm_info));
+
+    const ITensorInfo *matrix_a = a;
+    const ITensorInfo *matrix_b = b;
+    GEMMInfo           info     = gemm_info;
+
+    // Set internal variables
+    _a_offset                         = a->quantization_info().uniform().offset;
+    _b_offset                         = b->quantization_info().uniform().offset;
+    _run_vector_matrix_multiplication = a->dimension(1) < 2;
+    _reshape_b_only_on_first_run      = info.reshape_b_only_on_first_run();
+    _is_prepared                      = false;
+    _fused_assembly_path              = false;
+    _flip_signedness                  = is_data_type_quantized_per_channel(b->data_type()) && (a->data_type() == DataType::QASYMM8) && _reshape_b_only_on_first_run;
+    _gemm_info                        = gemm_info;
+
+    _asm_glue = std::make_unique<cpu::CpuGemmAssemblyDispatch>();
+
+    const ITensorInfo *a_to_use = a;
+
+    // Convert to QASYMM8 -> QASYMM8_SIGNED and back
+    if(_flip_signedness)
+    {
+        const int32_t                 offset_correction = 128;
+        const DataType                dt                = DataType::QASYMM8_SIGNED;
+        const UniformQuantizationInfo iqinfo            = a_to_use->quantization_info().uniform();
+
+        _signed_a                = a_to_use->clone()->set_data_type(dt).set_quantization_info(QuantizationInfo(iqinfo.scale, iqinfo.offset + offset_correction));
+        _convert_to_signed_asymm = std::make_unique<kernels::CpuConvertQuantizedSignednessKernel>();
+        _convert_to_signed_asymm->configure(a_to_use, &_signed_a);
+        a_to_use  = &_signed_a;
+        _a_offset = _signed_a.quantization_info().uniform().offset;
+
+        const UniformQuantizationInfo oqinfo = dst->quantization_info().uniform();
+        _signed_output                       = dst->clone()->set_data_type(dt).set_quantization_info(QuantizationInfo(oqinfo.scale, oqinfo.offset - offset_correction));
+
+        // Output stage correction
+        GEMMLowpOutputStageInfo output_stage_corr = info.gemmlowp_output_stage();
+        output_stage_corr.gemmlowp_offset         = _signed_output.quantization_info().uniform().offset;
+        output_stage_corr.gemmlowp_min_bound -= offset_correction;
+        output_stage_corr.gemmlowp_max_bound -= offset_correction;
+        info.set_gemmlowp_output_stage(output_stage_corr);
+
+        // Update matrix a
+        matrix_a = &_signed_a;
+    }
+
+    // If GEMMLowpOutputStage != NONE, fuse the offset contribution with the output stage
+    if(info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE)
+    {
+        _fuse_output_stage = true;
+        _mm_result_s32     = TensorInfo(dst->tensor_shape(), 1, DataType::S32);
+    }
+
+    // Initialize assembly kernel meta-data
+    const cpu::AsmGemmInfo asm_info = init_assembly_metadata(gemm_info);
+#ifdef __aarch64__
+    switch(a->data_type())
+    {
+        case DataType::QASYMM8:
+        case DataType::QASYMM8_SIGNED:
+        case DataType::U8:
+        case DataType::S8:
+        {
+            if(is_data_type_quantized_asymmetric(a_to_use->data_type()) && info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+            {
+                auto c_info_to_use = c == nullptr ? nullptr : c;
+                _asm_glue->configure(a_to_use, b, c_info_to_use, dst, asm_info);
+                _fused_assembly_path = _asm_glue->is_configured();
+            }
+            else
+            {
+                auto output_to_use = (_fuse_output_stage ? &_mm_result_s32 : dst);
+                _asm_glue->configure(a_to_use, b, nullptr, output_to_use, asm_info);
+            }
+            _assembly_path = _asm_glue->is_configured();
+            break;
+        }
+        default:
+        {
+            ARM_COMPUTE_ERROR("Datatype not supported");
+            break;
+        }
+    }
+#endif /* __aarch64__ */
+    if(!(_assembly_path || _run_vector_matrix_multiplication))
+    {
+        matrix_a = &_tmp_a;
+        matrix_b = &_tmp_b;
+
+        // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ]
+        _tmp_a = TensorInfo(compute_interleaved_shape(*a_to_use), 1, a_to_use->data_type(), a_to_use->quantization_info());
+        // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]
+        _tmp_b = TensorInfo(compute_transpose1xW_shape(*b), 1, b->data_type(), b->quantization_info());
+
+        // Configure interleave kernel
+        _mtx_a_reshape_kernel = std::make_unique<kernels::CpuGemmInterleave4x4Kernel>();
+        _mtx_a_reshape_kernel->configure(a_to_use, &_tmp_a);
+
+        // Configure transpose kernel
+        _mtx_b_reshape_kernel = std::make_unique<kernels::CpuGemmTranspose1xWKernel>();
+        _mtx_b_reshape_kernel->configure(b, &_tmp_b);
+    }
+
+    if(!_fused_assembly_path)
+    {
+        // Build reduction info
+        const GEMMLowpReductionKernelInfo reduction_info(a_to_use->dimension(0), false, 0, false);
+
+        // Initialize matrix B reduction kernel only if _a_offset is not equal to 0
+        if(_a_offset != 0)
+        {
+            _vector_sum_col = TensorInfo(compute_reductionA_shape(*b), 1, DataType::S32);
+
+            // Configure Matrix B reduction kernel
+            _mtx_b_reduction_kernel = std::make_unique<kernels::CpuGemmLowpMatrixBReductionKernel>();
+            _mtx_b_reduction_kernel->configure(b, &_vector_sum_col, reduction_info);
+        }
+
+        // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0
+        if(_b_offset != 0)
+        {
+            _vector_sum_row = TensorInfo(compute_reductionB_shape(*a_to_use), 1, DataType::S32);
+
+            // Configure matrix A reduction kernel
+            _mtx_a_reduction_kernel = std::make_unique<kernels::CpuGemmLowpMatrixAReductionKernel>();
+            _mtx_a_reduction_kernel->configure(a_to_use, &_vector_sum_row, reduction_info);
+        }
+
+        if(_fuse_output_stage)
+        {
+            // Configure matrix multiply kernel
+            if(!_assembly_path)
+            {
+                _mm_kernel = std::make_unique<kernels::CpuGemmLowpMatrixMultiplyKernel>();
+                _mm_kernel->configure(matrix_a, matrix_b, &_mm_result_s32);
+            }
+
+            _offset_contribution_output_stage_kernel = std::make_unique<kernels::CpuGemmLowpOffsetContributionOutputStageKernel>();
+            _offset_contribution_output_stage_kernel->configure(&_mm_result_s32,
+                                                                _a_offset == 0 ? nullptr : &_vector_sum_col,
+                                                                _b_offset == 0 ? nullptr : &_vector_sum_row, c,
+                                                                _flip_signedness ? &_signed_output : dst,
+                                                                a->dimension(0),
+                                                                _a_offset, _b_offset, info.gemmlowp_output_stage());
+
+            if(_flip_signedness)
+            {
+                _convert_from_signed_asymm = std::make_unique<kernels::CpuConvertQuantizedSignednessKernel>();
+                _convert_from_signed_asymm->configure(&_signed_output, dst);
+            }
+        }
+        else
+        {
+            // Configure matrix multiply kernel
+            if(!_assembly_path)
+            {
+                _mm_kernel = std::make_unique<kernels::CpuGemmLowpMatrixMultiplyKernel>();
+                _mm_kernel->configure(matrix_a, matrix_b, dst);
+            }
+            // Configure offset contribution kernel
+            _offset_contribution_kernel = std::make_unique<kernels::CpuGemmLowpOffsetContributionKernel>();
+            _offset_contribution_kernel->configure(dst, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, a_to_use->dimension(0),
+                                                   _a_offset, _b_offset);
+        }
+    }
+    // Configure activation
+    const ActivationLayerInfo &activation = gemm_info.activation_info();
+    _run_activation                       = activation.enabled() && (!_assembly_path || !cpu::CpuGemmAssemblyDispatch::is_activation_supported(activation));
+    if(_run_activation)
+    {
+        _activation_func = std::make_unique<CpuActivation>();
+        _activation_func->configure(dst, nullptr, activation);
+    }
+
+    if(_assembly_path)
+    {
+        auto asm_mem_req           = _asm_glue->workspace();
+        _aux_mem[AsmGemmWorkspace] = asm_mem_req[AsmGemmWorkspace];
+        _aux_mem[Pretranspose]     = asm_mem_req[Pretranspose];
+    }
+
+    // Request memory for LHS and RHS reshape matrix
+    _aux_mem[VectorSumCol] = MemoryInfo(offset_int_vec(VectorSumCol), !_fused_assembly_path && _a_offset != 0
+                                        && _reshape_b_only_on_first_run ?
+                                        MemoryLifetime::Persistent :
+                                        MemoryLifetime::Temporary,
+                                        _vector_sum_col.total_size());
+    _aux_mem[VectorSumRow] = MemoryInfo(offset_int_vec(VectorSumRow), MemoryLifetime::Temporary, _vector_sum_row.total_size());
+    _aux_mem[TmpA]         = MemoryInfo(offset_int_vec(TmpA), MemoryLifetime::Temporary, _tmp_a.total_size());
+    _aux_mem[TmpB]         = MemoryInfo(offset_int_vec(TmpB), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size());
+    _aux_mem[MMResultS32]  = MemoryInfo(offset_int_vec(MMResultS32), MemoryLifetime::Temporary, _mm_result_s32.total_size());
+    _aux_mem[SignedA]      = MemoryInfo(offset_int_vec(SignedA), MemoryLifetime::Temporary, _signed_a.total_size());
+    _aux_mem[SignedOutput] = MemoryInfo(offset_int_vec(SignedOutput), MemoryLifetime::Temporary, _signed_output.total_size());
+}
+
+Status CpuGemmLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(b, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(c != nullptr && gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::NONE, "Bias addition not supported in NEGEMMLowpMatrixMultiplyCore for output S32");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((a)->dimension(0) != (b)->dimension(1),
+                                    "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
+
+    GEMMInfo           info          = gemm_info;
+    const ITensorInfo *matrix_a_info = a;
+    const ITensorInfo *matrix_b_info = b;
+
+    const ITensorInfo *a_to_use = a;
+
+    TensorInfo tmp_a_info{};
+    TensorInfo tmp_b_info{};
+    TensorInfo mm_result_s32_info{};
+
+    int32_t a_offset = a->quantization_info().uniform().offset;
+    int32_t b_offset = b->quantization_info().uniform().offset;
+
+    bool fuse_output_stage = info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE;
+    if(fuse_output_stage)
+    {
+        auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(output->tensor_shape()).set_data_type(DataType::S32));
+    }
+
+    // Convert QASYMM8->QASYMM8_SIGNED
+    TensorInfo signed_a{};
+    TensorInfo signed_output{};
+    bool       flip_signedness = is_data_type_quantized_per_channel(b->data_type()) && (a->data_type() == DataType::QASYMM8) && info.reshape_b_only_on_first_run();
+    if(flip_signedness)
+    {
+        const int32_t                 offset_correction = 128;
+        const DataType                dt                = DataType::QASYMM8_SIGNED;
+        const UniformQuantizationInfo iqinfo            = a_to_use->quantization_info().uniform();
+
+        signed_a = a_to_use->clone()->set_data_type(dt).set_quantization_info(QuantizationInfo(iqinfo.scale, iqinfo.offset + offset_correction));
+        ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuConvertQuantizedSignednessKernel::validate(a_to_use, &signed_a));
+        a_to_use = &signed_a;
+        a_offset = signed_a.quantization_info().uniform().offset;
+
+        const UniformQuantizationInfo oqinfo = output->quantization_info().uniform();
+        signed_output                        = output->clone()->set_data_type(dt).set_quantization_info(QuantizationInfo(oqinfo.scale, oqinfo.offset - offset_correction));
+
+        // Output stage correction
+        GEMMLowpOutputStageInfo output_stage_corr = info.gemmlowp_output_stage();
+        output_stage_corr.gemmlowp_offset         = signed_output.quantization_info().uniform().offset;
+        output_stage_corr.gemmlowp_min_bound -= offset_correction;
+        output_stage_corr.gemmlowp_max_bound -= offset_correction;
+        info.set_gemmlowp_output_stage(output_stage_corr);
+
+        // Update matrix a
+        matrix_a_info = &signed_a;
+    }
+
+    // Initialize assembly kernel meta-data
+    const AsmGemmInfo asm_info = init_assembly_metadata(info);
+
+    // Check if we need to run the optimized assembly kernel
+    bool run_optimised             = false;
+    bool run_optimised_requantized = false;
+    if(is_data_type_quantized_asymmetric(a_to_use->data_type()) && info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+    {
+        run_optimised             = bool(CpuGemmAssemblyDispatch::validate(a_to_use, b, c, output, asm_info));
+        run_optimised_requantized = run_optimised;
+    }
+    else
+    {
+        run_optimised = bool(CpuGemmAssemblyDispatch::validate(a_to_use, b, nullptr, fuse_output_stage ? &mm_result_s32_info : output, asm_info));
+    }
+
+    if(run_optimised)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON(b->dimension(0) != output->dimension(0));
+        if(info.depth_output_gemm3d() != 0)
+        {
+            if(info.reinterpret_input_as_3d())
+            {
+                ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1));
+                ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(2) != output->dimension(2));
+            }
+            else
+            {
+                ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1) * output->dimension(2));
+            }
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_ON(a->dimension(1) != output->dimension(1));
+        }
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.reinterpret_input_as_3d(), "NEGEMM cannot reinterpret the input tensor as 3D");
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.depth_output_gemm3d() != 0, "NEGEMM cannot reinterpret the output tensor as 3D");
+
+        const bool run_vector_matrix_multiplication = a->dimension(1) < 2;
+        if(!run_vector_matrix_multiplication)
+        {
+            matrix_a_info = &tmp_a_info;
+            matrix_b_info = &tmp_b_info;
+
+            // The interleaved output matrix will have the following shape: [ a_height * 4, ceil(a_width / 4.0f) ]
+            TensorShape shape_tmp_a = a->tensor_shape();
+            shape_tmp_a.set(0, a->dimension(0) * 4);
+            shape_tmp_a.set(1, std::ceil(a->dimension(1) / 4.f));
+
+            // The transpose1xW output matrix will have the following shape: [ b_height * 16, ceil(b_width / 16.0f) ]
+            TensorShape shape_tmp_b = b->tensor_shape();
+            shape_tmp_b.set(0, b->dimension(1) * 16);
+            shape_tmp_b.set(1, std::ceil(b->dimension(0) / 16.f));
+
+            // Validate interleave kernel
+            auto_init_if_empty(tmp_a_info, a_to_use->clone()->set_tensor_shape(shape_tmp_a));
+            auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(shape_tmp_b));
+
+            ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmInterleave4x4Kernel::validate(a_to_use, &tmp_a_info));
+            ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmTranspose1xWKernel::validate(b, &tmp_b_info));
+        }
+    }
+
+    if(!run_optimised_requantized)
+    {
+        TensorInfo info_vector_sum_col{};
+        TensorInfo info_vector_sum_row{};
+
+        const GEMMLowpReductionKernelInfo reduction_info(a_to_use->dimension(0), false, 0, false);
+
+        // Validate matrix B reduction kernel only if _a_offset is not equal to 0
+        if(a_offset != 0)
+        {
+            info_vector_sum_col = TensorInfo(compute_reductionA_shape(*b), 1, DataType::S32);
+
+            // Configure Matrix B reduction kernel
+            ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpMatrixBReductionKernel::validate(b, &info_vector_sum_col, reduction_info));
+        }
+
+        // Validate Matrix A reduction kernel only if _b_offset is not equal to 0
+        if(b_offset != 0)
+        {
+            info_vector_sum_row = TensorInfo(compute_reductionB_shape(*a), 1, DataType::S32);
+
+            // Configure matrix A reduction kernel
+            ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpMatrixAReductionKernel::validate(a_to_use, &info_vector_sum_row, reduction_info));
+        }
+
+        if(fuse_output_stage)
+        {
+            if(!run_optimised)
+            {
+                ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.reinterpret_input_as_3d(), "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the input tensor as 3D");
+                ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.depth_output_gemm3d() != 0, "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the output tensor as 3D");
+
+                ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, &mm_result_s32_info));
+            }
+
+            // Validate offset contribution kernel
+            ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpOffsetContributionOutputStageKernel::validate(&mm_result_s32_info,
+                                                                                                          a_offset == 0 ? nullptr : &info_vector_sum_col,
+                                                                                                          b_offset == 0 ? nullptr : &info_vector_sum_row,
+                                                                                                          c,
+                                                                                                          flip_signedness ? &signed_output : output,
+                                                                                                          a_offset, b_offset,
+                                                                                                          info.gemmlowp_output_stage()));
+        }
+        else
+        {
+            if(!run_optimised)
+            {
+                ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.reinterpret_input_as_3d(), "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the input tensor as 3D");
+                ARM_COMPUTE_RETURN_ERROR_ON_MSG(info.depth_output_gemm3d() != 0, "CpuGemmLowpMatrixMultiplyKernel cannot reinterpret the output tensor as 3D");
+
+                ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpMatrixMultiplyKernel::validate(matrix_a_info, matrix_b_info, output));
+            }
+            // Validate offset contribution kernel
+            ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuGemmLowpOffsetContributionKernel::validate(output,
+                                                                                               a_offset == 0 ? nullptr : &info_vector_sum_col,
+                                                                                               b_offset == 0 ? nullptr : &info_vector_sum_row,
+                                                                                               a_offset, b_offset));
+        }
+    }
+
+    // Validate activation
+    const ActivationLayerInfo &activation = gemm_info.activation_info();
+    if(activation.enabled())
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CpuActivation::validate(output, nullptr, activation));
+    }
+
+    return Status{};
+}
+
+void CpuGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors)
+{
+    prepare(tensors);
+    auto a        = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+    auto b        = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+    auto c        = tensors.get_const_tensor(TensorType::ACL_SRC_2);
+    auto dst      = tensors.get_tensor(TensorType::ACL_DST);
+    auto a_to_use = a;
+    auto matrix_a = a;
+    auto matrix_b = b;
+
+    CpuAuxTensorHandler vector_sum_col(offset_int_vec(VectorSumCol), _vector_sum_col, tensors, false);
+    CpuAuxTensorHandler vector_sum_row(offset_int_vec(VectorSumRow), _vector_sum_row, tensors, false);
+    CpuAuxTensorHandler tmp_a(offset_int_vec(TmpA), _tmp_a, tensors, false);
+    CpuAuxTensorHandler tmp_b(offset_int_vec(TmpB), _tmp_b, tensors, true);
+    CpuAuxTensorHandler mm_result_s32(offset_int_vec(MMResultS32), _mm_result_s32, tensors, false);
+    CpuAuxTensorHandler signed_a(offset_int_vec(SignedA), _signed_a, tensors, false);
+    CpuAuxTensorHandler signed_output(offset_int_vec(SignedOutput), _signed_output, tensors, false);
+
+    // Convert QASYMM8->QASYMM8_SIGNED
+    if(_flip_signedness)
+    {
+        ITensorPack pack =
+        {
+            { TensorType::ACL_SRC, a },
+            { TensorType::ACL_DST, signed_a.get() }
+        };
+        NEScheduler::get().schedule_op(_convert_to_signed_asymm.get(), Window::DimY, _convert_to_signed_asymm->window(), pack);
+        a_to_use = signed_a.get();
+    }
+
+    // Run GEMM
+    if(_asm_glue->is_configured())
+    {
+        ITensorPack asm_glue_tensors = tensors;
+        auto        output_to_use    = (_fuse_output_stage ? mm_result_s32.get() : dst);
+        if(is_data_type_quantized_asymmetric(a_to_use->info()->data_type()) && _gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+        {
+            asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_0, a_to_use);
+            asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_1, b);
+            asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_2, c);
+            asm_glue_tensors.add_tensor(TensorType::ACL_DST, dst);
+        }
+        else
+        {
+            asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_0, a_to_use);
+            asm_glue_tensors.add_const_tensor(TensorType::ACL_SRC_1, b);
+            asm_glue_tensors.add_tensor(TensorType::ACL_DST, output_to_use);
+        }
+        _asm_glue->run(asm_glue_tensors);
+    }
+    else
+    {
+        if(!_run_vector_matrix_multiplication)
+        {
+            matrix_a = tmp_a.get();
+            matrix_b = tmp_b.get();
+            // Run interleave kernel
+            ITensorPack pack_a =
+            {
+                { TensorType::ACL_SRC, a_to_use },
+                { TensorType::ACL_DST, tmp_a.get() }
+            };
+            NEScheduler::get().schedule_op(_mtx_a_reshape_kernel.get(), Window::DimY, _mtx_a_reshape_kernel->window(), pack_a);
+
+            if(!_reshape_b_only_on_first_run)
+            {
+                ITensorPack pack_b =
+                {
+                    { TensorType::ACL_SRC, b },
+                    { TensorType::ACL_DST, tmp_b.get() }
+                };
+                // Run transpose kernel
+                NEScheduler::get().schedule_op(_mtx_b_reshape_kernel.get(), Window::DimY, _mtx_b_reshape_kernel->window(), pack_b);
+            }
+        }
+        ITensorPack pack_mm =
+        {
+            { TensorType::ACL_SRC_0, matrix_a },
+            { TensorType::ACL_SRC_1, matrix_b }
+        };
+        if(_fuse_output_stage)
+        {
+            pack_mm.add_tensor(TensorType::ACL_DST, mm_result_s32.get());
+        }
+        else
+        {
+            pack_mm.add_tensor(TensorType::ACL_DST, dst);
+        }
+        NEScheduler::get().schedule_op(_mm_kernel.get(), Window::DimY, _mm_kernel->window(), pack_mm);
+    }
+
+    if(!_fused_assembly_path)
+    {
+        // Run matrix A reduction kernel only if _b_offset is not equal to 0
+        if(_b_offset != 0)
+        {
+            ITensorPack pack =
+            {
+                { TensorType::ACL_SRC, a_to_use },
+                { TensorType::ACL_DST, vector_sum_row.get() }
+            };
+            NEScheduler::get().schedule_op(_mtx_a_reduction_kernel.get(), Window::DimX, _mtx_a_reduction_kernel->window(), pack);
+        }
+
+        // Run matrix B reduction kernel only if _a_offset is not equal to 0
+        if(_a_offset != 0 && !_reshape_b_only_on_first_run)
+        {
+            ITensorPack pack =
+            {
+                { TensorType::ACL_SRC, b },
+                { TensorType::ACL_DST, vector_sum_col.get() }
+            };
+            NEScheduler::get().schedule_op(_mtx_b_reduction_kernel.get(), Window::DimX, _mtx_b_reduction_kernel->window(), pack);
+        }
+
+        if(_fuse_output_stage)
+        {
+            ITensorPack pack;
+            pack.add_tensor(TensorType::ACL_SRC_0, mm_result_s32.get());
+            pack.add_tensor(TensorType::ACL_SRC_1, _a_offset == 0 ? nullptr : vector_sum_col.get());
+            pack.add_tensor(TensorType::ACL_SRC_2, _b_offset == 0 ? nullptr : vector_sum_row.get());
+            pack.add_tensor(TensorType::ACL_SRC_3, c);
+            pack.add_tensor(TensorType::ACL_DST, _flip_signedness ? signed_output.get() : dst);
+
+            // Run offset contribution kernel
+            NEScheduler::get().schedule_op(_offset_contribution_output_stage_kernel.get(), Window::DimY, _offset_contribution_output_stage_kernel->window(), pack);
+        }
+        else
+        {
+            ITensorPack pack;
+            pack.add_tensor(TensorType::ACL_SRC_0, _a_offset == 0 ? nullptr : vector_sum_col.get());
+            pack.add_tensor(TensorType::ACL_SRC_1, _b_offset == 0 ? nullptr : vector_sum_row.get());
+            pack.add_tensor(TensorType::ACL_DST, dst);
+
+            // Run offset contribution kernel
+            NEScheduler::get().schedule_op(_offset_contribution_kernel.get(), Window::DimY, _offset_contribution_kernel->window(), pack);
+        }
+    }
+
+    // Convert QASYMM8_SIGNED->QASYMM8
+    if(!_fused_assembly_path && _fuse_output_stage && _flip_signedness)
+    {
+        ITensorPack pack =
+        {
+            { TensorType::ACL_SRC, signed_output.get() },
+            { TensorType::ACL_DST, dst }
+        };
+        NEScheduler::get().schedule_op(_convert_from_signed_asymm.get(), Window::DimY, _convert_from_signed_asymm->window(), pack);
+    }
+
+    // Run fused activation unless already run in the fused assembly
+    if(_run_activation)
+    {
+        ITensorPack pack =
+        {
+            { TensorType::ACL_SRC, dst },
+            { TensorType::ACL_DST, dst }
+        };
+        _activation_func->run(pack);
+    }
+}
+
+void CpuGemmLowpMatrixMultiplyCore::prepare(ITensorPack &tensors)
+{
+    if(!_is_prepared)
+    {
+        auto original_b = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+        // Run assembly reshape
+        if(_asm_glue->is_configured())
+        {
+            _asm_glue->prepare(tensors);
+
+            auto has_reshape = std::find_if(_aux_mem.begin(),
+                                            _aux_mem.end(),
+                                            [](const MemoryInfo & m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });
+
+            if(has_reshape != std::end(_aux_mem))
+            {
+                original_b->mark_as_unused();
+            }
+        }
+        // Run non-assembly reshape
+        else if(_reshape_b_only_on_first_run && !_run_vector_matrix_multiplication && !_asm_glue->is_configured())
+        {
+            // Run reshape kernel and mark original weights tensor as unused
+            ITensor            *tmp_b_p = utils::cast::polymorphic_downcast<ITensor *>(tensors.get_tensor(offset_int_vec(TmpB)));
+            CpuAuxTensorHandler tmp_b(_tmp_b, *tmp_b_p);
+            ITensorPack         pack =
+            {
+                { TensorType::ACL_SRC, original_b },
+                { TensorType::ACL_DST, tmp_b.get() }
+            };
+            NEScheduler::get().schedule_op(_mtx_b_reshape_kernel.get(), Window::DimY, _mtx_b_reshape_kernel->window(), pack);
+        }
+
+        // Run matrix B reduction kernel only if _a_offset is not equal to 0
+        if(!_fused_assembly_path && _a_offset != 0 && _reshape_b_only_on_first_run)
+        {
+            ITensor            *vector_sum_col_p = utils::cast::polymorphic_downcast<ITensor *>(tensors.get_tensor(offset_int_vec(VectorSumCol)));
+            CpuAuxTensorHandler vector_sum_col(_vector_sum_col, *vector_sum_col_p);
+            ITensorPack         pack =
+            {
+                { TensorType::ACL_SRC, original_b },
+                { TensorType::ACL_DST, vector_sum_col.get() }
+            };
+            NEScheduler::get().schedule_op(_mtx_b_reduction_kernel.get(), Window::DimX, _mtx_b_reduction_kernel->window(), pack);
+        }
+        _is_prepared = true;
+    }
+}
+experimental::MemoryRequirements CpuGemmLowpMatrixMultiplyCore::workspace() const
+{
+    return _aux_mem;
+}
+} // namespace cpu
+} // namespace arm_compute