Port ClGemmLowp to new API

Signed-off-by: Georgios Pinitas <georgios.pinitas@arm.com>
Change-Id: Icef9ca564e61a00a3f4fd4ae7f465a711ff8c51d
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5939
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

7 files changed, 1007 insertions, 722 deletions

diff --git a/Android.bp b/Android.bp
index e5bb7a6a80..91dda75f51 100644
--- a/Android.bp
+++ b/Android.bp
@@ -672,6 +672,7 @@ cc_library_static {
         "src/runtime/gpu/cl/operators/ClFlatten.cpp",
         "src/runtime/gpu/cl/operators/ClFloor.cpp",
         "src/runtime/gpu/cl/operators/ClGemm.cpp",
+        "src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp",
         "src/runtime/gpu/cl/operators/ClGemmLowpOutputStage.cpp",
         "src/runtime/gpu/cl/operators/ClLogicalNot.cpp",
         "src/runtime/gpu/cl/operators/ClMul.cpp",
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
index e62db8e644..a8ee9e5b56 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
@@ -28,33 +28,14 @@
 #include "arm_compute/runtime/IFunction.h"
 #include "arm_compute/runtime/MemoryGroup.h"
 
+#include <memory>
+
 namespace arm_compute
 {
 class CLCompileContext;
 class IMemoryManager;
 class ICLTensor;
 class ITensorInfo;
-namespace opencl
-{
-namespace kernels
-{
-class ClGemmReshapeRhsMatrixKernel;
-class ClGemmLowpMatrixMultiplyNativeKernel;
-class ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel;
-class ClGemmLowpOffsetContributionKernel;
-class ClGemmLowpOffsetContributionOutputStageKernel;
-class ClGemmLowpMatrixAReductionKernel;
-class ClGemmLowpMatrixBReductionKernel;
-} // namespace kernels
-} // namespace opencl
-
-namespace opencl
-{
-namespace kernels
-{
-class ClCastKernel;
-} // namespace kernels
-} // namespace opencl
 
 /** Basic function to execute GEMMLowpMatrixMultiplyCore on OpenCL. */
 class CLGEMMLowpMatrixMultiplyCore : public IFunction
@@ -147,41 +128,8 @@ public:
     void prepare() override;
 
 private:
-    MemoryGroup _memory_group;
-
-    // Kernels used
-    std::unique_ptr<opencl::kernels::ClCastKernel>                                  _weights_to_qasymm8;
-    std::unique_ptr<opencl::kernels::ClGemmLowpMatrixMultiplyNativeKernel>          _mm_native_kernel;
-    std::unique_ptr<opencl::kernels::ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel> _mm_reshaped_only_rhs_kernel;
-    std::unique_ptr<opencl::kernels::ClGemmReshapeRhsMatrixKernel>                  _mtx_b_reshape_kernel;
-    std::unique_ptr<opencl::kernels::ClGemmLowpMatrixAReductionKernel>              _mtx_a_reduction_kernel;
-    std::unique_ptr<opencl::kernels::ClGemmLowpMatrixBReductionKernel>              _mtx_b_reduction_kernel;
-    std::unique_ptr<opencl::kernels::ClGemmLowpOffsetContributionKernel>            _offset_contribution_kernel;
-    std::unique_ptr<opencl::kernels::ClGemmLowpOffsetContributionOutputStageKernel> _offset_contribution_output_stage_kernel;
-
-    // Temporary tensors
-    CLTensor _qasymm8_weights;
-    CLTensor _vector_sum_col;
-    CLTensor _vector_sum_row;
-    CLTensor _tmp_b;
-    CLTensor _mm_result_s32;
-    CLTensor _gemm_output_stage_multipliers;
-    CLTensor _gemm_output_stage_shifts;
-
-    // Tensor pointers
-    const ICLTensor *_matrix_a;
-    const ICLTensor *_original_b;
-    const ICLTensor *_c;
-    ICLTensor       *_output;
-
-    int32_t _a_offset;
-    int32_t _b_offset;
-    bool    _is_gemm_reshaped;
-    bool    _reshape_b_only_on_first_run;
-    bool    _is_prepared;
-    bool    _run_output_stage;
-    bool    _convert_to_qasymm8;
-    bool    _run_offset_contribution;
+    struct Impl;
+    std::unique_ptr<Impl> _impl;
 };
 } // namespace arm_compute
 #endif /*ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYCORE_H */
\ No newline at end of file
diff --git a/filelist.json b/filelist.json
index e256744aab..914abc2ac3 100644
--- a/filelist.json
+++ b/filelist.json
@@ -275,6 +275,7 @@
       "GEMMLowp": {
         "files": {
           "operator": [
+            "src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp",
             "src/runtime/gpu/cl/operators/ClGemmLowpOutputStage.cpp"
           ],
           "kernel": [
diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
index 6c64731f73..bd31d47b4f 100644
--- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
+++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp
@@ -23,6 +23,7 @@
  */
 #include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
 
+#include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
@@ -31,193 +32,33 @@
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
-#include "src/core/gpu/cl/kernels/ClCastKernel.h"
-#include "src/core/gpu/cl/kernels/ClGemmLowpMatrixMultiplyNativeKernel.h"
-#include "src/core/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.h"
-#include "src/core/gpu/cl/kernels/ClGemmLowpOffsetContributionKernel.h"
-#include "src/core/gpu/cl/kernels/ClGemmLowpOffsetContributionOutputStageKernel.h"
-#include "src/core/gpu/cl/kernels/ClGemmLowpReductionKernel.h"
-#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h"
-#include "utils/TypePrinter.h"
+#include "arm_compute/runtime/IMemoryManager.h"
+#include "src/core/helpers/MemoryHelpers.h"
 
-namespace arm_compute
-{
-using namespace arm_compute::misc::shape_calculator;
-using namespace arm_compute::cl_gemm;
-using namespace arm_compute::opencl::kernels;
-
-namespace
-{
-inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type)
-{
-    switch(kernel_type)
-    {
-        case CLGEMMKernelType::NATIVE:
-        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
-        {
-            return true;
-        }
-        default:
-        {
-            return false;
-        }
-    }
-}
-//Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type
-inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run)
-{
-    auto gemm_kernel = auto_heuristics::select_mlgo_gemm_kernel(query, reshape_b_only_on_first_run);
-    if(bool(gemm_kernel))
-    {
-        if(validate_gemm_kernel(gemm_kernel.gemm_type))
-        {
-            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from mlgo heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str());
-            return gemm_kernel.gemm_type;
-        }
-    }
-    gemm_kernel = auto_heuristics::select_default_gemm_kernel(query, reshape_b_only_on_first_run);
-    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from default heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str());
-    return gemm_kernel.gemm_type;
-}
-// Validate lhs_info and rhs_info for native kernel
-inline bool validate_lhs_rhs_info_native(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const GEMMReshapeInfo &reshape_info)
-{
-    // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel
-    TensorInfo mm_result_s32_info{};
-    // Output tensor auto initialization if not yet initialized
-    auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(compute_mm_shape(*a, *b, false, reshape_info)).set_data_type(DataType::S32));
-    // Validate mm kernel
-    // NOTE: Ignore all other parameters (eg. output stage etc.) and only validate lhs and rhs info
-    // NOTE: This assumes:
-    //  1. lhs and rhs info's validity does not depend on these other parameters and vice versa(in CLGEMMLowpMatrixMultiplyNativeKernel.cpp validate_arguments).
-    //  2. lhs and rhs info does not cause window and padding issues through side effects (in CLGEMMLowpMatrixMultiplyNativeKernel.cpp validate_and_configure_window).
-    if(!bool(ClGemmLowpMatrixMultiplyNativeKernel::validate(a, b, &mm_result_s32_info, lhs_info, rhs_info, reshape_info)))
-    {
-        return false;
-    }
-    return true;
-}
-
-// Automatically select between mlgo (prioritized) and default heuristics for native kernel configs
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_native(auto_heuristics::CommonQuery query, const ITensorInfo *a, const ITensorInfo *b, const GEMMReshapeInfo &reshape_info)
-{
-    auto config = auto_heuristics::select_mlgo_gemm_config_native(query);
-    if(config)
-    {
-        if(validate_lhs_rhs_info_native(config.lhs_info, config.rhs_info, a, b, reshape_info))
-        {
-            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use native config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
-            return { config.lhs_info, config.rhs_info };
-        }
-    }
-    config = auto_heuristics::select_default_gemm_config_native(query);
-    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use native config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
-    return { config.lhs_info, config.rhs_info };
-}
+#include "src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h"
 
-// Validate lhs_info and rhs_info for reshaped only rhs kernel
-inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output,
-                                                    unsigned int m, unsigned int n, unsigned int k, bool reinterpret_input_as_3d, int depth_output_gemm3d)
-{
-    // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel
-    TensorInfo tmp_b_info{};
-    // Validate reshape RHS kernel
-    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
-    if(!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)))
-    {
-        return false;
-    }
-    // Validate mm kernel
-    // NOTE: Ignore all other parameters (eg. depth_output_gemm3d, output stage etc.) and only validate lhs and rhs info
-    // NOTE: This assumes:
-    //  1. lhs and rhs info's validity does not depend on these other parameters and vice versa(in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_arguments).
-    //  2. lhs and rhs info does not cause window and padding issues through side effects (in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_and_configure_window).
-    GEMMKernelInfo gemm_kernel_info;
-    gemm_kernel_info.m                       = m;
-    gemm_kernel_info.n                       = n;
-    gemm_kernel_info.k                       = k;
-    gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
-    gemm_kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    gemm_kernel_info.lhs_info                = lhs_info;
-    gemm_kernel_info.rhs_info                = rhs_info;
-    // Since we ignore the output stage, output data type has to be S32 to pass the validation
-    TensorInfo output_info_copy(*output);
-    output_info_copy.set_data_type(DataType::S32);
-    if(!bool(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, &output_info_copy, gemm_kernel_info)))
-    {
-        return false;
-    }
-    return true;
-}
-
-// Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs
-std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, bool reinterpret_input_as_3d, int depth_output_gemm3d,
-                                                                                          const ITensorInfo *a,
-                                                                                          const ITensorInfo *b, const ITensorInfo *output)
+namespace arm_compute
 {
-    auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(query);
-    if(config)
-    {
-        if(validate_lhs_rhs_info_reshaped_only_rhs(config.lhs_info, config.rhs_info, a, b, output, query.m, query.n, query.k, reinterpret_input_as_3d, depth_output_gemm3d))
-        {
-            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
-            return { config.lhs_info, config.rhs_info };
-        }
-    }
-    config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query);
-    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
-    return { config.lhs_info, config.rhs_info };
-}
+using namespace arm_compute::experimental;
+using OperatorType = opencl::ClGemmLowpMatrixMultiplyCore;
 
-inline bool is_gemm_reshaped(CLGEMMKernelType kernel_type)
+struct CLGEMMLowpMatrixMultiplyCore::Impl
 {
-    switch(kernel_type)
-    {
-        case CLGEMMKernelType::NATIVE:
-            return false;
-        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
-            return true;
-        default:
-            ARM_COMPUTE_ERROR("Not supported gemmlowp kernel!");
-    }
-}
-} // namespace
+    const ICLTensor              *b{ nullptr };
+    std::unique_ptr<OperatorType> op{ nullptr };
+    MemoryGroup                   memory_group{};
+    ITensorPack                   run_pack{};
+    MemoryRequirements            aux_mem_req{};
+    WorkspaceData<CLTensor>       workspace_tensors{};
+    bool                          is_prepared{ false };
+};
 
 CLGEMMLowpMatrixMultiplyCore::CLGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)),
-      _weights_to_qasymm8(std::make_unique<ClCastKernel>()),
-      _mm_native_kernel(std::make_unique<ClGemmLowpMatrixMultiplyNativeKernel>()),
-      _mm_reshaped_only_rhs_kernel(std::make_unique<ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel>()),
-      _mtx_b_reshape_kernel(std::make_unique<ClGemmReshapeRhsMatrixKernel>()),
-      _mtx_a_reduction_kernel(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
-      _mtx_b_reduction_kernel(std::make_unique<ClGemmLowpMatrixBReductionKernel>()),
-      _offset_contribution_kernel(std::make_unique<ClGemmLowpOffsetContributionKernel>()),
-      _offset_contribution_output_stage_kernel(std::make_unique<ClGemmLowpOffsetContributionOutputStageKernel>()),
-      _qasymm8_weights(),
-      _vector_sum_col(),
-      _vector_sum_row(),
-      _tmp_b(),
-      _mm_result_s32(),
-      _gemm_output_stage_multipliers(),
-      _gemm_output_stage_shifts(),
-      _matrix_a(nullptr),
-      _original_b(nullptr),
-      _c(nullptr),
-      _output(nullptr),
-      _a_offset(0),
-      _b_offset(0),
-      _is_gemm_reshaped(true),
-      _reshape_b_only_on_first_run(false),
-      _is_prepared(false),
-      _run_output_stage(false),
-      _convert_to_qasymm8(false),
-      _run_offset_contribution(false)
+    : _impl(std::make_unique<Impl>())
 {
+    _impl->memory_group = MemoryGroup(memory_manager);
 }
 
 CLGEMMLowpMatrixMultiplyCore::~CLGEMMLowpMatrixMultiplyCore() = default;
@@ -230,528 +71,77 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor
 void CLGEMMLowpMatrixMultiplyCore::configure(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, const GEMMInfo &gemm_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
-    ARM_COMPUTE_ERROR_THROW_ON(CLGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), gemm_info));
-
-    _is_prepared                 = false;
-    _original_b                  = b;
-    _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
-    _a_offset                    = a->info()->quantization_info().uniform().offset;
-    _matrix_a                    = a;
-    _c                           = c;
-    _output                      = output;
-
-    _convert_to_qasymm8 = is_data_type_quantized_per_channel(b->info()->data_type()) && is_data_type_quantized_symmetric(b->info()->data_type())
-                          && a->info()->data_type() == DataType::QASYMM8;
-    _b_offset = _convert_to_qasymm8 ? -128 : b->info()->quantization_info().uniform().offset;
-
-    // Get the GPU target
-    const GPUTarget gpu_target = CLScheduler::get().target();
-
-    // Set the target for the kernels
-    _mm_native_kernel->set_target(gpu_target);
-    _mm_reshaped_only_rhs_kernel->set_target(gpu_target);
-
-    GEMMRHSMatrixInfo rhs_info;
-    GEMMLHSMatrixInfo lhs_info;
-
-    // Arguments used by GEMMReshapeInfo
-    // If we pass the matrix A and matrix B reshaped to CLGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to CLGEMMReshapeInfo
-    // in order to know how the matrices have been reshaped
-    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
-    const unsigned int m                       = reinterpret_input_as_3d ? (a->info()->dimension(1) * a->info()->dimension(2)) : a->info()->dimension(1);
-    const unsigned int n                       = b->info()->dimension(0);
-    const unsigned int k                       = a->info()->dimension(0);
-    const unsigned int batch_size              = reinterpret_input_as_3d ? a->info()->dimension(3) : a->info()->dimension(2);
-    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
-
-    const auto reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d);
 
-    // Check if we need to reshape the matrix A and matrix B
-    _is_gemm_reshaped = is_gemm_reshaped(auto_select_gemm_kernel(auto_heuristics::CommonQuery{ gpu_target, a->info()->data_type(), m, n, k, batch_size }, _reshape_b_only_on_first_run));
+    _impl->b           = b;
+    _impl->op          = std::make_unique<OperatorType>();
+    _impl->is_prepared = gemm_info.retain_internal_weights();
 
-    if(_convert_to_qasymm8)
-    {
-        // Set data type for converted weights
-        TensorInfo weights_info(*b->info());
-        weights_info.set_data_type(DataType::QASYMM8);
-        _qasymm8_weights.allocator()->init(weights_info);
-        _weights_to_qasymm8->configure(compile_context, b->info(), _qasymm8_weights.info(), ConvertPolicy::WRAP);
-    }
-
-    const ICLTensor *matrix_b = _convert_to_qasymm8 ? &_qasymm8_weights : b;
-    if(_is_gemm_reshaped)
-    {
-        matrix_b = &_tmp_b;
-
-        if(!_reshape_b_only_on_first_run)
-        {
-            _memory_group.manage(&_tmp_b);
-        }
-
-        // Pick up the GEMM configuration
-        // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
-        std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size }, reinterpret_input_as_3d,
-                                                                                 depth_output_gemm3d,
-                                                                                 a->info(), _convert_to_qasymm8 ? _qasymm8_weights.info() : b->info(), output->info());
-
-        // Configure reshape RHS kernel
-        _mtx_b_reshape_kernel->configure(compile_context, _convert_to_qasymm8 ? _qasymm8_weights.info() : b->info(), _tmp_b.info(), rhs_info);
-    }
-
-    // Using default reduction info
-    const GEMMLowpReductionKernelInfo reduction_info {};
-
-    // Initialize matrix B reduction kernel only if _a_offset is not equal to 0
-    if(_a_offset != 0)
-    {
-        TensorInfo info_vector_sum_col(compute_reductionA_shape(*b->info()), 1, DataType::S32);
-        _vector_sum_col.allocator()->init(info_vector_sum_col);
-        if(!_reshape_b_only_on_first_run)
-        {
-            _memory_group.manage(&_vector_sum_col);
-        }
-
-        // Configure Matrix B reduction kernel
-        _mtx_b_reduction_kernel->configure(compile_context, _convert_to_qasymm8 ? _qasymm8_weights.info() : b->info(), _vector_sum_col.info(), reduction_info);
-    }
+    _impl->op->configure(compile_context, a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), gemm_info);
+    _impl->aux_mem_req = _impl->op->workspace();
 
-    // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0
-    if(_b_offset != 0)
+    // Manage/allocate auxilairy tensors
+    if(_impl->is_prepared)
     {
-        TensorInfo info_vector_sum_row(compute_reductionB_shape(*a->info()), 1, DataType::S32);
-        _vector_sum_row.allocator()->init(info_vector_sum_row);
-        _memory_group.manage(&_vector_sum_row);
-
-        // Configure matrix A reduction kernel
-        _mtx_a_reduction_kernel->configure(compile_context, a->info(), _vector_sum_row.info(), reduction_info);
-    }
-
-    GEMMKernelInfo gemm_kernel_info;
-    gemm_kernel_info.m                       = m;
-    gemm_kernel_info.n                       = n;
-    gemm_kernel_info.k                       = k;
-    gemm_kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
-    gemm_kernel_info.lhs_info                = lhs_info;
-    gemm_kernel_info.rhs_info                = rhs_info;
-    gemm_kernel_info.a_offset                = _a_offset;
-    gemm_kernel_info.b_offset                = _b_offset;
-    // If GEMMLowpOutputStage != NONE, fuse the offset contribution with the output stage
-    if(gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE)
-    {
-        // Configure offset contribution kernel
-        const size_t num_filters = (gemm_info.gemmlowp_output_stage().is_quantized_per_channel) ? gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() : 1;
-
-        _gemm_output_stage_multipliers.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
-        _gemm_output_stage_shifts.allocator()->init(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
-
-        GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage();
-        gemmlowp_output_stage.output_data_type        = _matrix_a->info()->data_type();
-        if(num_filters == 1)
-        {
-            // Per-channel quantization with OFM == 1 is equivalent to uniform quantization.
-            // Setting this flag to false prevents the kernel from adding useless padding to the output multipliers and shifts
-            gemmlowp_output_stage.is_quantized_per_channel = false;
-        }
-
-        gemm_kernel_info.output_stage = gemmlowp_output_stage;
-
-        if(_is_gemm_reshaped && gemmlowp_output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
-        {
-            // Configure and tune matrix multiply kernel with fused output stage
-            _mm_reshaped_only_rhs_kernel->configure(compile_context, _matrix_a->info(), matrix_b->info(), output->info(), gemm_kernel_info, _a_offset == 0 ? nullptr : _vector_sum_col.info(),
-                                                    _b_offset == 0 ? nullptr : _vector_sum_row.info(), c != nullptr ? c->info() : nullptr, _gemm_output_stage_multipliers.info(), _gemm_output_stage_shifts.info());
-        }
-        else
-        {
-            _run_output_stage = true;
-
-            _memory_group.manage(&_mm_result_s32);
-
-            if(_is_gemm_reshaped)
-            {
-                _mm_reshaped_only_rhs_kernel->configure(compile_context, _matrix_a->info(), matrix_b->info(), _mm_result_s32.info(), gemm_kernel_info);
-            }
-            else
-            {
-                // Pick up the GEMM configuration
-                // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
-                std::tie(lhs_info, rhs_info) = auto_select_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size },
-                                                                              _matrix_a->info(), _convert_to_qasymm8 ? _qasymm8_weights.info() : matrix_b->info(), reshape_info);
-
-                // Configure matrix multiply kernel
-                _mm_native_kernel->configure(compile_context, _matrix_a->info(), matrix_b->info(), _mm_result_s32.info(), lhs_info, rhs_info, reshape_info);
-
-                _offset_contribution_output_stage_kernel->configure(compile_context, _mm_result_s32.info(), _a_offset == 0 ? nullptr : _vector_sum_col.info(), _b_offset == 0 ? nullptr : _vector_sum_row.info(),
-                                                                    c != nullptr ? c->info() : nullptr, output->info(), a->info()->dimension(0), _a_offset, _b_offset, gemmlowp_output_stage,
-                                                                    _gemm_output_stage_multipliers.info(), _gemm_output_stage_shifts.info());
-                _mm_result_s32.allocator()->allocate();
-            }
-        }
-
-        _gemm_output_stage_multipliers.allocator()->allocate();
-        _gemm_output_stage_shifts.allocator()->allocate();
-        // Compute GEMM output multipliers and shifts for output stage
-        _gemm_output_stage_multipliers.map();
-        _gemm_output_stage_shifts.map();
-        std::memcpy(_gemm_output_stage_multipliers.ptr_to_element(Coordinates(0)), gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.data(), num_filters * sizeof(int32_t));
-        std::memcpy(_gemm_output_stage_shifts.ptr_to_element(Coordinates(0)), gemm_info.gemmlowp_output_stage().gemmlowp_shifts.data(), num_filters * sizeof(int32_t));
-        _gemm_output_stage_multipliers.unmap();
-        _gemm_output_stage_shifts.unmap();
+        _impl->run_pack.add_const_tensor(ACL_SRC_0, a);
+        _impl->run_pack.add_tensor(ACL_DST, output);
     }
     else
     {
-        _run_offset_contribution = true;
-        if(_is_gemm_reshaped)
-        {
-            // Configure and tune matrix multiply kernel
-            _mm_reshaped_only_rhs_kernel->configure(compile_context, _matrix_a->info(), matrix_b->info(), output->info(), gemm_kernel_info);
-        }
-        else
-        {
-            // Pick up the GEMM configuration
-            // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
-            std::tie(lhs_info, rhs_info) = auto_select_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size },
-                                                                          a->info(), _convert_to_qasymm8 ? _qasymm8_weights.info() : b->info(), reshape_info);
-
-            // Configure matrix multiply kernel
-            _mm_native_kernel->configure(compile_context, _matrix_a->info(), matrix_b->info(), output->info(), lhs_info, rhs_info, reshape_info);
-        }
-
-        // Configure offset contribution kernel
-        _offset_contribution_kernel->configure(compile_context, output->info(), _a_offset == 0 ? nullptr : _vector_sum_col.info(), _b_offset == 0 ? nullptr : _vector_sum_row.info(),
-                                               c != nullptr ? c->info() : nullptr, a->info()->dimension(0), _a_offset, _b_offset);
-    }
-
-    // Allocate tensors
-    if(_is_gemm_reshaped)
-    {
-        if(!_reshape_b_only_on_first_run)
-        {
-            _tmp_b.allocator()->allocate();
-        }
-    }
-
-    if(_a_offset != 0 && !_reshape_b_only_on_first_run)
-    {
-        _vector_sum_col.allocator()->allocate();
-    }
-
-    if(_b_offset != 0)
-    {
-        _vector_sum_row.allocator()->allocate();
+        _impl->run_pack          = { { ACL_SRC_0, a }, { ACL_SRC_1, _impl->b }, { ACL_SRC_2, c }, { ACL_DST, output } };
+        _impl->workspace_tensors = manage_workspace<CLTensor>(_impl->op->workspace(), _impl->memory_group, _impl->run_pack, _impl->run_pack);
     }
 }
 
 Status CLGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info)
 {
-    ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
-    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(a->data_type() == DataType::QASYMM8 && b->data_type() == DataType::QASYMM8_SIGNED);
-    ARM_COMPUTE_RETURN_ERROR_ON(a->data_type() == DataType::QASYMM8_SIGNED && b->data_type() == DataType::QASYMM8);
-    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");
-
-    int32_t a_offset = a->quantization_info().uniform().offset;
-    int32_t b_offset = b->quantization_info().uniform().offset;
-
-    const ITensorInfo *matrix_a_info = a;
-
-    TensorInfo        tmp_b_info{};
-    GEMMRHSMatrixInfo rhs_info;
-    GEMMLHSMatrixInfo lhs_info;
-
-    // Get the GPU target
-    const GPUTarget gpu_target = CLScheduler::get().target();
-
-    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
-    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
-    const unsigned int n                       = b->dimension(0);
-    const unsigned int k                       = a->dimension(0);
-    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
-    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
-
-    bool reshape_matrix_b = is_gemm_reshaped(auto_select_gemm_kernel(auto_heuristics::CommonQuery{ gpu_target, a->data_type(), m, n, k, batch_size }, gemm_info.reshape_b_only_on_first_run()));
-
-    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d);
-
-    bool convert_to_qasymm8 = is_data_type_quantized_per_channel(b->data_type()) && is_data_type_quantized_symmetric(b->data_type())
-                              && is_data_type_quantized_asymmetric(a->data_type());
-    TensorInfo weights_info(*b);
-    if(convert_to_qasymm8)
-    {
-        b_offset = -128;
-        weights_info.set_data_type(DataType::QASYMM8);
-        ARM_COMPUTE_RETURN_ON_ERROR(ClCastKernel::validate(b, &weights_info, ConvertPolicy::WRAP));
-    }
-    const ITensorInfo *matrix_b_info = &weights_info;
-    if(reshape_matrix_b)
-    {
-        matrix_b_info = &tmp_b_info;
-
-        // Pick up the GEMM configuration
-        // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails
-        // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
-        const auto res = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size });
-        lhs_info       = res.lhs_info;
-        rhs_info       = res.rhs_info;
-
-        // Validate reshape RHS kernel
-        auto_init_if_empty(tmp_b_info, weights_info.clone()->set_tensor_shape(compute_rhs_reshaped_shape(weights_info, rhs_info)));
-        ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(&weights_info, &tmp_b_info, rhs_info));
-    }
-
-    TensorInfo info_vector_sum_col{};
-    TensorInfo info_vector_sum_row{};
-
-    const GEMMLowpReductionKernelInfo reduction_info;
-    // 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(weights_info), 1, DataType::S32);
-
-        // Configure Matrix B reduction kernel
-        ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixBReductionKernel::validate(&weights_info, &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(ClGemmLowpMatrixAReductionKernel::validate(a, &info_vector_sum_row, reduction_info));
-    }
-
-    GEMMKernelInfo gemm_kernel_info;
-    gemm_kernel_info.m                       = m;
-    gemm_kernel_info.n                       = n;
-    gemm_kernel_info.k                       = k;
-    gemm_kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
-    gemm_kernel_info.lhs_info                = lhs_info;
-    gemm_kernel_info.rhs_info                = rhs_info;
-    gemm_kernel_info.a_offset                = a_offset;
-    gemm_kernel_info.b_offset                = b_offset;
-    if(gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE)
-    {
-        const size_t num_filters = (gemm_info.gemmlowp_output_stage().is_quantized_per_channel) ? gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() : 1;
-
-        const TensorInfo gemm_output_stage_multipliers_shifts_info(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
-
-        GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage();
-        gemmlowp_output_stage.output_data_type        = a->data_type();
-
-        gemm_kernel_info.output_stage = gemmlowp_output_stage;
-        if(reshape_matrix_b && gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(matrix_a_info, matrix_b_info, output, gemm_kernel_info,
-                                                                                                a_offset == 0 ? nullptr : &info_vector_sum_col,
-                                                                                                b_offset == 0 ? nullptr : &info_vector_sum_row,
-                                                                                                c,
-                                                                                                &gemm_output_stage_multipliers_shifts_info,
-                                                                                                &gemm_output_stage_multipliers_shifts_info));
-        }
-        else
-        {
-            TensorInfo mm_result_s32_info{};
-
-            if(reshape_matrix_b)
-            {
-                // Output tensor auto inizialitation if not yet initialized
-                auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(compute_mm_shape(*matrix_a_info, *matrix_b_info, reshape_info)).set_data_type(DataType::S32));
-
-                // Validate matrix multiply
-                ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(matrix_a_info, matrix_b_info, &mm_result_s32_info, gemm_kernel_info));
-            }
-            else
-            {
-                // Output tensor auto inizialitation if not yet initialized
-                auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(compute_mm_shape(*matrix_a_info, *matrix_b_info, false, reshape_info)).set_data_type(DataType::S32));
-
-                // Pick up the GEMM configuration
-                // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails
-                // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
-                const auto res = select_default_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size });
-                lhs_info       = res.lhs_info;
-                rhs_info       = res.rhs_info;
-
-                // Validate matrix multiply
-                ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyNativeKernel::validate(matrix_a_info, matrix_b_info, &mm_result_s32_info, lhs_info, rhs_info, reshape_info));
-            }
-
-            // Validate offset contribution kernel
-            ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpOffsetContributionOutputStageKernel::validate(&mm_result_s32_info,
-                                                                                                a_offset == 0 ? nullptr : &info_vector_sum_col,
-                                                                                                b_offset == 0 ? nullptr : &info_vector_sum_row,
-                                                                                                c,
-                                                                                                output,
-                                                                                                a_offset, b_offset,
-                                                                                                gemmlowp_output_stage,
-                                                                                                &gemm_output_stage_multipliers_shifts_info,
-                                                                                                &gemm_output_stage_multipliers_shifts_info));
-        }
-    }
-    else
-    {
-        if(reshape_matrix_b)
-        {
-            // Validate matrix multiply
-            ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(matrix_a_info, matrix_b_info, output, gemm_kernel_info));
-        }
-        else
-        {
-            // Pick up the GEMM configuration
-            // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
-            const auto res = select_default_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size });
-            lhs_info       = res.lhs_info;
-            rhs_info       = res.rhs_info;
-
-            // Validate matrix multiply
-            ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyNativeKernel::validate(matrix_a_info, matrix_b_info, output, lhs_info, rhs_info, reshape_info));
-        }
-
-        if(output->total_size() != 0)
-        {
-            // Validate offset contribution kernel
-            ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpOffsetContributionKernel::validate(output,
-                                                                                     a_offset == 0 ? nullptr : &info_vector_sum_col,
-                                                                                     b_offset == 0 ? nullptr : &info_vector_sum_row,
-                                                                                     c,
-                                                                                     a_offset, b_offset));
-        }
-    }
-
-    return Status{};
+    return OperatorType::validate(a, b, c, output, gemm_info);
 }
 
 void CLGEMMLowpMatrixMultiplyCore::run()
 {
     prepare();
 
-    MemoryGroupResourceScope scope_mg(_memory_group);
-
-    const ICLTensor *matrix_b = _convert_to_qasymm8 ? &_qasymm8_weights : _original_b;
-
-    if(_is_gemm_reshaped)
-    {
-        matrix_b = &_tmp_b;
-        if(!_reshape_b_only_on_first_run)
-        {
-            // Run reshape matrix B
-            ITensorPack mtx_b_reshape_pack =
-            {
-                { TensorType::ACL_SRC, _convert_to_qasymm8 ? &_qasymm8_weights : _original_b },
-                { TensorType::ACL_DST, &_tmp_b }
-            };
-            CLScheduler::get().enqueue_op(*_mtx_b_reshape_kernel, mtx_b_reshape_pack, false);
-        }
-    }
-
-    // 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 mtx_b_red_pack =
-        {
-            { TensorType::ACL_SRC, _convert_to_qasymm8 ? &_qasymm8_weights : _original_b },
-            { TensorType::ACL_DST, &_vector_sum_col }
-        };
-        CLScheduler::get().enqueue_op(*_mtx_b_reduction_kernel, mtx_b_red_pack, false);
-    }
-
-    // Run matrix A reduction kernel only if _b_offset is not equal to 0
-    if(_b_offset != 0)
-    {
-        ITensorPack mtx_a_red_pack = { { TensorType::ACL_SRC, _matrix_a }, { TensorType::ACL_DST, &_vector_sum_row } };
-        CLScheduler::get().enqueue_op(*_mtx_a_reduction_kernel, mtx_a_red_pack, false);
-    }
+    MemoryGroupResourceScope scope_mg(_impl->memory_group);
 
-    // Run matrix multiply
-    if(_is_gemm_reshaped)
-    {
-        ITensorPack gemm_reshaped_pack;
-        if(_run_offset_contribution)
-        {
-            gemm_reshaped_pack = ITensorPack({ { TensorType::ACL_SRC_0, _matrix_a }, { TensorType::ACL_SRC_1, matrix_b }, { TensorType::ACL_DST, _run_output_stage ? &_mm_result_s32 : _output } });
-        }
-        else
-        {
-            gemm_reshaped_pack = ITensorPack(
-            {
-                { TensorType::ACL_SRC, _matrix_a }, { TensorType::ACL_SRC_1, matrix_b }, { TensorType::ACL_BIAS, _c }, { TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr : &_vector_sum_row }, { TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr : &_vector_sum_col }, { TensorType::ACL_SHIFTS, &_gemm_output_stage_shifts }, { TensorType::ACL_MULTIPLIERS, &_gemm_output_stage_multipliers }, { TensorType::ACL_DST, _output },
-            });
-        }
-        CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_kernel, gemm_reshaped_pack, false);
-    }
-    else
-    {
-        ITensorPack gemm_native_pack =
-        {
-            { TensorType::ACL_SRC_0, _matrix_a }, { TensorType::ACL_SRC_1, matrix_b }, { TensorType::ACL_DST, _run_offset_contribution ? _output :&_mm_result_s32 }
-        };
-        CLScheduler::get().enqueue_op(*_mm_native_kernel, gemm_native_pack, false);
-    }
-    if(_run_output_stage)
-    {
-        // Run offset contribution/output stage kernel
-        ITensorPack output_stage_pack =
-        {
-            { TensorType::ACL_SRC, &_mm_result_s32 }, { TensorType::ACL_BIAS, _c }, { TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr :&_vector_sum_row }, { TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr :&_vector_sum_col }, { TensorType::ACL_SHIFTS, &_gemm_output_stage_shifts }, { TensorType::ACL_MULTIPLIERS, &_gemm_output_stage_multipliers }, { TensorType::ACL_DST, _output },
-        };
-        CLScheduler::get().enqueue_op(*_offset_contribution_output_stage_kernel, output_stage_pack, true);
-    }
-    if(_run_offset_contribution)
-    {
-        // Run offset contribution kernel
-        ITensorPack offset_contrib_pack =
-        {
-            { TensorType::ACL_SRC_DST, _output }, { TensorType::ACL_BIAS, _c }, { TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr :&_vector_sum_row }, { TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr :&_vector_sum_col }
-        };
-        CLScheduler::get().enqueue_op(*_offset_contribution_kernel, offset_contrib_pack, true);
-    }
+    _impl->op->run(_impl->run_pack);
 }
 
 void CLGEMMLowpMatrixMultiplyCore::prepare()
 {
-    if(!_is_prepared)
+    if(!_impl->is_prepared)
     {
-        if(_convert_to_qasymm8)
+        _impl->op->prepare(_impl->run_pack);
+
+        auto has_reshape = std::find_if(_impl->aux_mem_req.begin(),
+                                        _impl->aux_mem_req.end(),
+                                        [](const MemoryInfo & m) -> bool { return m.lifetime == MemoryLifetime::Persistent; });
+
+        if(has_reshape != std::end(_impl->aux_mem_req))
         {
-            _qasymm8_weights.allocator()->allocate();
-            ITensorPack convert_to_qs8_pack = { { ACL_SRC, _original_b }, { ACL_DST, &_qasymm8_weights } };
-            CLScheduler::get().enqueue_op(*_weights_to_qasymm8, convert_to_qs8_pack, false);
+            _impl->b->mark_as_unused();
         }
-
-        if(_is_gemm_reshaped && _reshape_b_only_on_first_run)
+        else
         {
-            ARM_COMPUTE_ERROR_ON(!_original_b->is_used());
-
-            // Run reshape kernel and mark original weights tensor as unused
-            _tmp_b.allocator()->allocate();
-            ITensorPack mtx_b_pack =
-            {
-                { TensorType::ACL_SRC, _convert_to_qasymm8 ? &_qasymm8_weights : _original_b },
-                { TensorType::ACL_DST, &_tmp_b }
-            };
-            CLScheduler::get().enqueue_op(*_mtx_b_reshape_kernel, mtx_b_pack, false);
-            _original_b->mark_as_unused();
+            // Pack the B matrix to be used as the underlying GEMM performs no reshapes
+            _impl->run_pack.add_const_tensor(ACL_SRC_1, _impl->b);
         }
 
-        // Run matrix B reduction kernel only if _a_offset is not equal to 0
-        if(_a_offset != 0 && _reshape_b_only_on_first_run)
+        // Release temporary tensors that are only used in prepare stage
+        for(auto &ws : _impl->workspace_tensors)
         {
-            _vector_sum_col.allocator()->allocate();
-            ITensorPack mtx_b_red_pack =
+            const int slot = ws.slot;
+            for(auto &m : _impl->aux_mem_req)
             {
-                { TensorType::ACL_SRC, _convert_to_qasymm8 ? &_qasymm8_weights : _original_b },
-                { TensorType::ACL_DST, &_vector_sum_col }
-            };
-            CLScheduler::get().enqueue_op(*_mtx_b_reduction_kernel, mtx_b_red_pack, false);
+                if(m.slot == slot && m.lifetime == MemoryLifetime::Prepare)
+                {
+                    auto tensor = ws.tensor.get();
+                    tensor->allocator()->free();
+                    break;
+                }
+            }
         }
 
-        CLScheduler::get().queue().finish();
-        _is_prepared = true;
+        _impl->is_prepared = true;
     }
 }
 } // namespace arm_compute
diff --git a/src/runtime/gpu/cl/operators/ClGemm.h b/src/runtime/gpu/cl/operators/ClGemm.h
index bd9ca17edf..aad208bdb0 100644
--- a/src/runtime/gpu/cl/operators/ClGemm.h
+++ b/src/runtime/gpu/cl/operators/ClGemm.h
@@ -27,6 +27,7 @@
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
 #include "arm_compute/runtime/CL/CLTypes.h"
+
 #include "src/core/gpu/cl/ClCompileContext.h"
 #include "src/core/gpu/cl/IClKernel.h"
 #include "src/core/gpu/cl/kernels/ClGemmMatrixMultiplyKernel.h"
diff --git a/src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp b/src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp
new file mode 100644
index 0000000000..82047ad2a5
--- /dev/null
+++ b/src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.cpp
@@ -0,0 +1,789 @@
+/*
+ * Copyright (c) 2017-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h"
+
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/KernelDescriptors.h"
+#include "arm_compute/core/Log.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+
+#include "src/core/gpu/cl/kernels/ClCastKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmLowpMatrixMultiplyNativeKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmLowpOffsetContributionKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmLowpOffsetContributionOutputStageKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmLowpReductionKernel.h"
+#include "src/core/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/MemoryHelpers.h"
+#include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h"
+#include "src/runtime/gpu/cl/utils/ClAuxTensorHandler.h"
+
+#include "utils/TypePrinter.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+using namespace arm_compute::misc::shape_calculator;
+using namespace arm_compute::cl_gemm;
+using namespace arm_compute::opencl::kernels;
+using namespace arm_compute::experimental;
+
+namespace
+{
+inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type)
+{
+    switch(kernel_type)
+    {
+        case CLGEMMKernelType::NATIVE:
+        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
+        {
+            return true;
+        }
+        default:
+        {
+            return false;
+        }
+    }
+}
+
+//Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type
+inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run)
+{
+    auto gemm_kernel = auto_heuristics::select_mlgo_gemm_kernel(query, reshape_b_only_on_first_run);
+    if(bool(gemm_kernel))
+    {
+        if(validate_gemm_kernel(gemm_kernel.gemm_type))
+        {
+            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from mlgo heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str());
+            return gemm_kernel.gemm_type;
+        }
+    }
+    gemm_kernel = auto_heuristics::select_default_gemm_kernel(query, reshape_b_only_on_first_run);
+    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from default heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str());
+    return gemm_kernel.gemm_type;
+}
+
+// Validate lhs_info and rhs_info for native kernel
+inline bool validate_lhs_rhs_info_native(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const GEMMReshapeInfo &reshape_info)
+{
+    // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel
+    TensorInfo mm_result_s32_info{};
+    // Output tensor auto initialization if not yet initialized
+    auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(compute_mm_shape(*a, *b, false, reshape_info)).set_data_type(DataType::S32));
+    // Validate mm kernel
+    // NOTE: Ignore all other parameters (eg. output stage etc.) and only validate lhs and rhs info
+    // NOTE: This assumes:
+    //  1. lhs and rhs info's validity does not depend on these other parameters and vice versa(in CLGEMMLowpMatrixMultiplyNativeKernel.cpp validate_arguments).
+    //  2. lhs and rhs info does not cause window and padding issues through side effects (in CLGEMMLowpMatrixMultiplyNativeKernel.cpp validate_and_configure_window).
+    if(!bool(ClGemmLowpMatrixMultiplyNativeKernel::validate(a, b, &mm_result_s32_info, lhs_info, rhs_info, reshape_info)))
+    {
+        return false;
+    }
+    return true;
+}
+
+// Automatically select between mlgo (prioritized) and default heuristics for native kernel configs
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_native(auto_heuristics::CommonQuery query, const ITensorInfo *a, const ITensorInfo *b, const GEMMReshapeInfo &reshape_info)
+{
+    auto config = auto_heuristics::select_mlgo_gemm_config_native(query);
+    if(config)
+    {
+        if(validate_lhs_rhs_info_native(config.lhs_info, config.rhs_info, a, b, reshape_info))
+        {
+            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use native config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
+            return { config.lhs_info, config.rhs_info };
+        }
+    }
+    config = auto_heuristics::select_default_gemm_config_native(query);
+    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use native config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
+    return { config.lhs_info, config.rhs_info };
+}
+
+// Validate lhs_info and rhs_info for reshaped only rhs kernel
+inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output,
+                                                    unsigned int m, unsigned int n, unsigned int k, bool reinterpret_input_as_3d, int depth_output_gemm3d)
+{
+    // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel
+    TensorInfo tmp_b_info{};
+    // Validate reshape RHS kernel
+    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
+    if(!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info)))
+    {
+        return false;
+    }
+    // Validate mm kernel
+    // NOTE: Ignore all other parameters (eg. depth_output_gemm3d, output stage etc.) and only validate lhs and rhs info
+    // NOTE: This assumes:
+    //  1. lhs and rhs info's validity does not depend on these other parameters and vice versa(in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_arguments).
+    //  2. lhs and rhs info does not cause window and padding issues through side effects (in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_and_configure_window).
+    GEMMKernelInfo gemm_kernel_info;
+    gemm_kernel_info.m                       = m;
+    gemm_kernel_info.n                       = n;
+    gemm_kernel_info.k                       = k;
+    gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
+    gemm_kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
+    gemm_kernel_info.lhs_info                = lhs_info;
+    gemm_kernel_info.rhs_info                = rhs_info;
+    // Since we ignore the output stage, output data type has to be S32 to pass the validation
+    TensorInfo output_info_copy(*output);
+    output_info_copy.set_data_type(DataType::S32);
+    if(!bool(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, &output_info_copy, gemm_kernel_info)))
+    {
+        return false;
+    }
+    return true;
+}
+
+// Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs
+std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, bool reinterpret_input_as_3d, int depth_output_gemm3d,
+                                                                                          const ITensorInfo *a,
+                                                                                          const ITensorInfo *b, const ITensorInfo *output)
+{
+    auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(query);
+    if(config)
+    {
+        if(validate_lhs_rhs_info_reshaped_only_rhs(config.lhs_info, config.rhs_info, a, b, output, query.m, query.n, query.k, reinterpret_input_as_3d, depth_output_gemm3d))
+        {
+            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
+            return { config.lhs_info, config.rhs_info };
+        }
+    }
+    config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query);
+    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str());
+    return { config.lhs_info, config.rhs_info };
+}
+
+inline bool is_gemm_reshaped(CLGEMMKernelType kernel_type)
+{
+    switch(kernel_type)
+    {
+        case CLGEMMKernelType::NATIVE:
+            return false;
+        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
+            return true;
+        default:
+            ARM_COMPUTE_ERROR("Not supported gemmlowp kernel!");
+    }
+}
+} // namespace
+
+ClGemmLowpMatrixMultiplyCore::ClGemmLowpMatrixMultiplyCore()
+    : _weights_to_qasymm8(std::make_unique<ClCastKernel>()),
+      _mm_native_kernel(std::make_unique<ClGemmLowpMatrixMultiplyNativeKernel>()),
+      _mm_reshaped_only_rhs_kernel(std::make_unique<ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel>()),
+      _mtx_b_reshape_kernel(std::make_unique<ClGemmReshapeRhsMatrixKernel>()),
+      _mtx_a_reduction_kernel(std::make_unique<ClGemmLowpMatrixAReductionKernel>()),
+      _mtx_b_reduction_kernel(std::make_unique<ClGemmLowpMatrixBReductionKernel>()),
+      _offset_contribution_kernel(std::make_unique<ClGemmLowpOffsetContributionKernel>()),
+      _offset_contribution_output_stage_kernel(std::make_unique<ClGemmLowpOffsetContributionOutputStageKernel>()),
+      _aux_mem(AuxTensorIdx::Count)
+{
+}
+
+ClGemmLowpMatrixMultiplyCore::~ClGemmLowpMatrixMultiplyCore() = default;
+
+void ClGemmLowpMatrixMultiplyCore::configure(const CLCompileContext &compile_context,
+                                             ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output,
+                                             const GEMMInfo &gemm_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
+    ARM_COMPUTE_ERROR_THROW_ON(ClGemmLowpMatrixMultiplyCore::validate(a, b, c != nullptr ? c : nullptr, output, gemm_info));
+
+    _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
+    _a_offset                    = a->quantization_info().uniform().offset;
+    _convert_to_qasymm8          = is_data_type_quantized_per_channel(b->data_type()) && is_data_type_quantized_symmetric(b->data_type())
+                                   && a->data_type() == DataType::QASYMM8;
+    _b_offset  = _convert_to_qasymm8 ? -128 : b->quantization_info().uniform().offset;
+    _gemm_info = gemm_info;
+
+    // Get the GPU target
+    const GPUTarget gpu_target = CLScheduler::get().target();
+
+    // Set the target for the kernels
+    _mm_native_kernel->set_target(gpu_target);
+    _mm_reshaped_only_rhs_kernel->set_target(gpu_target);
+
+    GEMMRHSMatrixInfo rhs_info;
+    GEMMLHSMatrixInfo lhs_info;
+
+    // Arguments used by GEMMReshapeInfo
+    // If we pass the matrix A and matrix B reshaped to CLGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to CLGEMMReshapeInfo
+    // in order to know how the matrices have been reshaped
+    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                       = b->dimension(0);
+    const unsigned int k                       = a->dimension(0);
+    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
+    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
+
+    const auto reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d);
+
+    // Check if we need to reshape the matrix A and matrix B
+    _is_gemm_reshaped = is_gemm_reshaped(auto_select_gemm_kernel(auto_heuristics::CommonQuery{ gpu_target, a->data_type(), m, n, k, batch_size }, _reshape_b_only_on_first_run));
+
+    if(_convert_to_qasymm8)
+    {
+        // Set data type for converted weights
+        _qasymm8_weights = *b;
+        _qasymm8_weights.set_data_type(DataType::QASYMM8);
+        _weights_to_qasymm8->configure(compile_context, b, &_qasymm8_weights, ConvertPolicy::WRAP);
+    }
+
+    ITensorInfo *matrix_b = _convert_to_qasymm8 ? &_qasymm8_weights : b;
+    if(_is_gemm_reshaped)
+    {
+        matrix_b = &_tmp_b;
+
+        // Pick up the GEMM configuration
+        // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
+        std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size }, reinterpret_input_as_3d,
+                                                                                 depth_output_gemm3d,
+                                                                                 a, _convert_to_qasymm8 ? &_qasymm8_weights : b, output);
+
+        // Configure reshape RHS kernel
+        _mtx_b_reshape_kernel->configure(compile_context, _convert_to_qasymm8 ? &_qasymm8_weights : b, &_tmp_b, rhs_info);
+    }
+
+    // Using default reduction info
+    const GEMMLowpReductionKernelInfo reduction_info {};
+
+    // 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->configure(compile_context, _convert_to_qasymm8 ? &_qasymm8_weights : 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), 1, DataType::S32);
+
+        // Configure matrix A reduction kernel
+        _mtx_a_reduction_kernel->configure(compile_context, a, &_vector_sum_row, reduction_info);
+    }
+
+    GEMMKernelInfo gemm_kernel_info;
+    gemm_kernel_info.m                       = m;
+    gemm_kernel_info.n                       = n;
+    gemm_kernel_info.k                       = k;
+    gemm_kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
+    gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
+    gemm_kernel_info.lhs_info                = lhs_info;
+    gemm_kernel_info.rhs_info                = rhs_info;
+    gemm_kernel_info.a_offset                = _a_offset;
+    gemm_kernel_info.b_offset                = _b_offset;
+    // If GEMMLowpOutputStage != NONE, fuse the offset contribution with the output stage
+    if(gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE)
+    {
+        // Configure offset contribution kernel
+        const size_t num_filters = (gemm_info.gemmlowp_output_stage().is_quantized_per_channel) ? gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() : 1;
+
+        _gemm_output_stage_multipliers = TensorInfo(TensorShape(num_filters), 1, DataType::S32);
+        _gemm_output_stage_shifts      = TensorInfo(TensorShape(num_filters), 1, DataType::S32);
+
+        GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage();
+        gemmlowp_output_stage.output_data_type        = a->data_type();
+        if(num_filters == 1)
+        {
+            // Per-channel quantization with OFM == 1 is equivalent to uniform quantization.
+            // Setting this flag to false prevents the kernel from adding useless padding to the output multipliers and shifts
+            gemmlowp_output_stage.is_quantized_per_channel = false;
+        }
+
+        gemm_kernel_info.output_stage = gemmlowp_output_stage;
+
+        if(_is_gemm_reshaped && gemmlowp_output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+        {
+            // Configure and tune matrix multiply kernel with fused output stage
+            _mm_reshaped_only_rhs_kernel->configure(compile_context, a, matrix_b, output, gemm_kernel_info, _a_offset == 0 ? nullptr : &_vector_sum_col,
+                                                    _b_offset == 0 ? nullptr : &_vector_sum_row, c != nullptr ? c : nullptr, &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts);
+        }
+        else
+        {
+            _run_output_stage = true;
+
+            // _memory_group.manage(&_mm_result_s32);
+
+            if(_is_gemm_reshaped)
+            {
+                _mm_reshaped_only_rhs_kernel->configure(compile_context, a, matrix_b, &_mm_result_s32, gemm_kernel_info);
+            }
+            else
+            {
+                // Pick up the GEMM configuration
+                // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
+                std::tie(lhs_info, rhs_info) = auto_select_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size },
+                                                                              a, _convert_to_qasymm8 ? &_qasymm8_weights : matrix_b, reshape_info);
+
+                // Configure matrix multiply kernel
+                _mm_native_kernel->configure(compile_context, a, matrix_b, &_mm_result_s32, lhs_info, rhs_info, reshape_info);
+
+                _offset_contribution_output_stage_kernel->configure(compile_context, &_mm_result_s32, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row,
+                                                                    c != nullptr ? c : nullptr, output, a->dimension(0), _a_offset, _b_offset, gemmlowp_output_stage,
+                                                                    &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts);
+            }
+        }
+    }
+    else
+    {
+        _run_offset_contribution = true;
+        if(_is_gemm_reshaped)
+        {
+            // Configure and tune matrix multiply kernel
+            _mm_reshaped_only_rhs_kernel->configure(compile_context, a, matrix_b, output, gemm_kernel_info);
+        }
+        else
+        {
+            // Pick up the GEMM configuration
+            // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
+            std::tie(lhs_info, rhs_info) = auto_select_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size },
+                                                                          a, _convert_to_qasymm8 ? &_qasymm8_weights : b, reshape_info);
+
+            // Configure matrix multiply kernel
+            _mm_native_kernel->configure(compile_context, a, matrix_b, output, lhs_info, rhs_info, reshape_info);
+        }
+
+        // Configure offset contribution kernel
+        _offset_contribution_kernel->configure(compile_context, output, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row,
+                                               c != nullptr ? c : nullptr, a->dimension(0), _a_offset, _b_offset);
+    }
+
+    // Request memory
+    _aux_mem[RhsQAsymm8] = MemoryInfo(offset_int_vec(RhsQAsymm8), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _qasymm8_weights.total_size());
+    if(_is_gemm_reshaped)
+    {
+        // Overwrite Rhs as prepare if gemm is reshaped as there will be a two-step transformation
+        _aux_mem[RhsQAsymm8] = MemoryInfo(offset_int_vec(RhsQAsymm8), _reshape_b_only_on_first_run ? MemoryLifetime::Prepare : MemoryLifetime::Temporary, _qasymm8_weights.total_size());
+        _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size());
+    }
+    if(_a_offset != 0)
+    {
+        _aux_mem[VecSumCol] = MemoryInfo(offset_int_vec(VecSumCol), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _vector_sum_col.total_size());
+    }
+    if(_b_offset != 0)
+    {
+        _aux_mem[VecSumRow] = MemoryInfo(offset_int_vec(VecSumRow), MemoryLifetime::Temporary, _vector_sum_row.total_size());
+    }
+    _aux_mem[ResultS32]   = MemoryInfo(offset_int_vec(ResultS32), MemoryLifetime::Temporary, _mm_result_s32.total_size());
+    _aux_mem[Multipliers] = MemoryInfo(offset_int_vec(Multipliers), MemoryLifetime::Persistent, _gemm_output_stage_multipliers.total_size());
+    _aux_mem[Shifts]      = MemoryInfo(offset_int_vec(Shifts), MemoryLifetime::Persistent, _gemm_output_stage_shifts.total_size());
+}
+
+Status ClGemmLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
+    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(a->data_type() == DataType::QASYMM8 && b->data_type() == DataType::QASYMM8_SIGNED);
+    ARM_COMPUTE_RETURN_ERROR_ON(a->data_type() == DataType::QASYMM8_SIGNED && b->data_type() == DataType::QASYMM8);
+    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");
+
+    int32_t a_offset = a->quantization_info().uniform().offset;
+    int32_t b_offset = b->quantization_info().uniform().offset;
+
+    const ITensorInfo *matrix_a_info = a;
+
+    TensorInfo        tmp_b_info{};
+    GEMMRHSMatrixInfo rhs_info;
+    GEMMLHSMatrixInfo lhs_info;
+
+    // Get the GPU target
+    const GPUTarget gpu_target = CLScheduler::get().target();
+
+    bool               reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+    const unsigned int m                       = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1);
+    const unsigned int n                       = b->dimension(0);
+    const unsigned int k                       = a->dimension(0);
+    const unsigned int batch_size              = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2);
+    const int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
+
+    bool reshape_matrix_b = is_gemm_reshaped(auto_select_gemm_kernel(auto_heuristics::CommonQuery{ gpu_target, a->data_type(), m, n, k, batch_size }, gemm_info.reshape_b_only_on_first_run()));
+
+    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d);
+
+    bool convert_to_qasymm8 = is_data_type_quantized_per_channel(b->data_type()) && is_data_type_quantized_symmetric(b->data_type())
+                              && is_data_type_quantized_asymmetric(a->data_type());
+    TensorInfo weights_info(*b);
+    if(convert_to_qasymm8)
+    {
+        b_offset = -128;
+        weights_info.set_data_type(DataType::QASYMM8);
+        ARM_COMPUTE_RETURN_ON_ERROR(ClCastKernel::validate(b, &weights_info, ConvertPolicy::WRAP));
+    }
+    const ITensorInfo *matrix_b_info = &weights_info;
+    if(reshape_matrix_b)
+    {
+        matrix_b_info = &tmp_b_info;
+
+        // Pick up the GEMM configuration
+        // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails
+        // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
+        const auto res = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size });
+        lhs_info       = res.lhs_info;
+        rhs_info       = res.rhs_info;
+
+        // Validate reshape RHS kernel
+        auto_init_if_empty(tmp_b_info, weights_info.clone()->set_tensor_shape(compute_rhs_reshaped_shape(weights_info, rhs_info)));
+        ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(&weights_info, &tmp_b_info, rhs_info));
+    }
+
+    TensorInfo info_vector_sum_col{};
+    TensorInfo info_vector_sum_row{};
+
+    const GEMMLowpReductionKernelInfo reduction_info;
+    // 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(weights_info), 1, DataType::S32);
+
+        // Configure Matrix B reduction kernel
+        ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixBReductionKernel::validate(&weights_info, &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(ClGemmLowpMatrixAReductionKernel::validate(a, &info_vector_sum_row, reduction_info));
+    }
+
+    GEMMKernelInfo gemm_kernel_info;
+    gemm_kernel_info.m                       = m;
+    gemm_kernel_info.n                       = n;
+    gemm_kernel_info.k                       = k;
+    gemm_kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
+    gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
+    gemm_kernel_info.lhs_info                = lhs_info;
+    gemm_kernel_info.rhs_info                = rhs_info;
+    gemm_kernel_info.a_offset                = a_offset;
+    gemm_kernel_info.b_offset                = b_offset;
+    if(gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE)
+    {
+        const size_t num_filters = (gemm_info.gemmlowp_output_stage().is_quantized_per_channel) ? gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() : 1;
+
+        const TensorInfo gemm_output_stage_multipliers_shifts_info(TensorInfo(TensorShape(num_filters), 1, DataType::S32));
+
+        GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage();
+        gemmlowp_output_stage.output_data_type        = a->data_type();
+
+        gemm_kernel_info.output_stage = gemmlowp_output_stage;
+        if(reshape_matrix_b && gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT)
+        {
+            ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(matrix_a_info, matrix_b_info, output, gemm_kernel_info,
+                                                                                                a_offset == 0 ? nullptr : &info_vector_sum_col,
+                                                                                                b_offset == 0 ? nullptr : &info_vector_sum_row,
+                                                                                                c,
+                                                                                                &gemm_output_stage_multipliers_shifts_info,
+                                                                                                &gemm_output_stage_multipliers_shifts_info));
+        }
+        else
+        {
+            TensorInfo mm_result_s32_info{};
+
+            if(reshape_matrix_b)
+            {
+                // Output tensor auto inizialitation if not yet initialized
+                auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(compute_mm_shape(*matrix_a_info, *matrix_b_info, reshape_info)).set_data_type(DataType::S32));
+
+                // Validate matrix multiply
+                ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(matrix_a_info, matrix_b_info, &mm_result_s32_info, gemm_kernel_info));
+            }
+            else
+            {
+                // Output tensor auto inizialitation if not yet initialized
+                auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(compute_mm_shape(*matrix_a_info, *matrix_b_info, false, reshape_info)).set_data_type(DataType::S32));
+
+                // Pick up the GEMM configuration
+                // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails
+                // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
+                const auto res = select_default_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size });
+                lhs_info       = res.lhs_info;
+                rhs_info       = res.rhs_info;
+
+                // Validate matrix multiply
+                ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyNativeKernel::validate(matrix_a_info, matrix_b_info, &mm_result_s32_info, lhs_info, rhs_info, reshape_info));
+            }
+
+            // Validate offset contribution kernel
+            ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpOffsetContributionOutputStageKernel::validate(&mm_result_s32_info,
+                                                                                                a_offset == 0 ? nullptr : &info_vector_sum_col,
+                                                                                                b_offset == 0 ? nullptr : &info_vector_sum_row,
+                                                                                                c,
+                                                                                                output,
+                                                                                                a_offset, b_offset,
+                                                                                                gemmlowp_output_stage,
+                                                                                                &gemm_output_stage_multipliers_shifts_info,
+                                                                                                &gemm_output_stage_multipliers_shifts_info));
+        }
+    }
+    else
+    {
+        if(reshape_matrix_b)
+        {
+            // Validate matrix multiply
+            ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(matrix_a_info, matrix_b_info, output, gemm_kernel_info));
+        }
+        else
+        {
+            // Pick up the GEMM configuration
+            // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration
+            const auto res = select_default_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size });
+            lhs_info       = res.lhs_info;
+            rhs_info       = res.rhs_info;
+
+            // Validate matrix multiply
+            ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyNativeKernel::validate(matrix_a_info, matrix_b_info, output, lhs_info, rhs_info, reshape_info));
+        }
+
+        if(output->total_size() != 0)
+        {
+            // Validate offset contribution kernel
+            ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpOffsetContributionKernel::validate(output,
+                                                                                     a_offset == 0 ? nullptr : &info_vector_sum_col,
+                                                                                     b_offset == 0 ? nullptr : &info_vector_sum_row,
+                                                                                     c,
+                                                                                     a_offset, b_offset));
+        }
+    }
+
+    return Status{};
+}
+
+void ClGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors)
+{
+    const ITensor *a   = tensors.get_const_tensor(ACL_SRC_0);
+    const ITensor *b   = tensors.get_const_tensor(ACL_SRC_1);
+    const ITensor *c   = tensors.get_const_tensor(ACL_SRC_2);
+    ITensor       *dst = tensors.get_tensor(ACL_DST);
+
+    ARM_COMPUTE_ERROR_ON_NULLPTR(a, dst);
+
+    CLAuxTensorHandler vec_sum_col(offset_int_vec(VecSumCol), _vector_sum_col, tensors, true);
+    CLAuxTensorHandler vec_sum_row(offset_int_vec(VecSumRow), _vector_sum_row, tensors, true);
+    CLAuxTensorHandler rhs_qasymm8(offset_int_vec(RhsQAsymm8), _qasymm8_weights, tensors, true);
+    CLAuxTensorHandler tmp_b(offset_int_vec(RhsReshape), _tmp_b, tensors, true);
+    CLAuxTensorHandler res32(offset_int_vec(ResultS32), _mm_result_s32, tensors, true);
+    CLAuxTensorHandler shifts(offset_int_vec(Shifts), _gemm_output_stage_shifts, tensors, true);
+    CLAuxTensorHandler multipliers(offset_int_vec(Multipliers), _gemm_output_stage_multipliers, tensors, true);
+
+    // Prepare the consts if needed
+    prepare(tensors);
+
+    const ITensor *matrix_a = a;
+    const ITensor *matrix_b = _convert_to_qasymm8 ? rhs_qasymm8.get() : b;
+
+    if(_is_gemm_reshaped)
+    {
+        matrix_b = tmp_b.get();
+        if(!_reshape_b_only_on_first_run)
+        {
+            // Run reshape matrix B
+            ITensorPack mtx_b_reshape_pack =
+            {
+                { TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b },
+                { TensorType::ACL_DST, tmp_b.get() }
+            };
+            CLScheduler::get().enqueue_op(*_mtx_b_reshape_kernel, mtx_b_reshape_pack, false);
+        }
+    }
+
+    // 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 mtx_b_red_pack =
+        {
+            { TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b },
+            { TensorType::ACL_DST, vec_sum_col.get() }
+        };
+        CLScheduler::get().enqueue_op(*_mtx_b_reduction_kernel, mtx_b_red_pack, false);
+    }
+
+    // Run matrix A reduction kernel only if _b_offset is not equal to 0
+    if(_b_offset != 0)
+    {
+        ITensorPack mtx_a_red_pack =
+        {
+            { TensorType::ACL_SRC, matrix_a },
+            { TensorType::ACL_DST, vec_sum_row.get() }
+        };
+        CLScheduler::get().enqueue_op(*_mtx_a_reduction_kernel, mtx_a_red_pack, false);
+    }
+
+    // Run matrix multiply
+    if(_is_gemm_reshaped)
+    {
+        ITensorPack gemm_reshaped_pack;
+        if(_run_offset_contribution)
+        {
+            gemm_reshaped_pack = ITensorPack({ { TensorType::ACL_SRC_0, matrix_a },
+                { TensorType::ACL_SRC_1, matrix_b },
+                { TensorType::ACL_DST, _run_output_stage ? res32.get() : dst }
+            });
+        }
+        else
+        {
+            gemm_reshaped_pack = ITensorPack(
+            {
+                { TensorType::ACL_SRC, matrix_a },
+                { TensorType::ACL_SRC_1, matrix_b },
+                { TensorType::ACL_BIAS, c },
+                { TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr : vec_sum_row.get() },
+                { TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr : vec_sum_col.get() },
+                { TensorType::ACL_SHIFTS, shifts.get() },
+                { TensorType::ACL_MULTIPLIERS, multipliers.get() },
+                { TensorType::ACL_DST, dst },
+            });
+        }
+        CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_kernel, gemm_reshaped_pack, false);
+    }
+    else
+    {
+        ITensorPack gemm_native_pack =
+        {
+            { TensorType::ACL_SRC_0, matrix_a },
+            { TensorType::ACL_SRC_1, matrix_b },
+            { TensorType::ACL_DST, _run_offset_contribution ? dst : res32.get() }
+        };
+        CLScheduler::get().enqueue_op(*_mm_native_kernel, gemm_native_pack, false);
+    }
+    if(_run_output_stage)
+    {
+        // Run offset contribution/output stage kernel
+        ITensorPack output_stage_pack =
+        {
+            { TensorType::ACL_SRC, res32.get() },
+            { TensorType::ACL_BIAS, c },
+            { TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr : vec_sum_row.get() },
+            { TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr : vec_sum_col.get() },
+            { TensorType::ACL_SHIFTS, shifts.get() },
+            { TensorType::ACL_MULTIPLIERS, multipliers.get() },
+            { TensorType::ACL_DST, dst },
+        };
+        CLScheduler::get().enqueue_op(*_offset_contribution_output_stage_kernel, output_stage_pack, true);
+    }
+    if(_run_offset_contribution)
+    {
+        // Run offset contribution kernel
+        ITensorPack offset_contrib_pack =
+        {
+            { TensorType::ACL_SRC_DST, dst },
+            { TensorType::ACL_BIAS, c },
+            { TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr : vec_sum_row.get() },
+            { TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr : vec_sum_col.get() }
+        };
+        CLScheduler::get().enqueue_op(*_offset_contribution_kernel, offset_contrib_pack, true);
+    }
+}
+
+void ClGemmLowpMatrixMultiplyCore::prepare(ITensorPack &tensors)
+{
+    if(!_is_prepared)
+    {
+        auto               b = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+        CLAuxTensorHandler tmp_b(offset_int_vec(RhsReshape), _tmp_b, tensors, true);
+        CLAuxTensorHandler vec_sum_col(offset_int_vec(VecSumCol), _vector_sum_col, tensors, true);
+        CLAuxTensorHandler rhs_qasymm8(offset_int_vec(RhsQAsymm8), _qasymm8_weights, tensors, false);
+
+        ARM_COMPUTE_ERROR_ON_NULLPTR(b);
+
+        if(_convert_to_qasymm8)
+        {
+            ITensorPack convert_to_qs8_pack = { { ACL_SRC, b }, { ACL_DST, rhs_qasymm8.get() } };
+            CLScheduler::get().enqueue_op(*_weights_to_qasymm8, convert_to_qs8_pack, false);
+        }
+
+        if(_is_gemm_reshaped && _reshape_b_only_on_first_run)
+        {
+            ARM_COMPUTE_ERROR_ON(!b->is_used());
+
+            // Run reshape kernel and mark original weights tensor as unused
+            ITensorPack mtx_b_pack =
+            {
+                { TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b },
+                { TensorType::ACL_DST, tmp_b.get() }
+            };
+            CLScheduler::get().enqueue_op(*_mtx_b_reshape_kernel, mtx_b_pack, false);
+            b->mark_as_unused();
+        }
+
+        // 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 mtx_b_red_pack =
+            {
+                { TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b },
+                { TensorType::ACL_DST, vec_sum_col.get() }
+            };
+            CLScheduler::get().enqueue_op(*_mtx_b_reduction_kernel, mtx_b_red_pack, false);
+        }
+
+        // Compute GEMM output multipliers and shifts for output stage
+        {
+            const size_t num_filters = (_gemm_info.gemmlowp_output_stage().is_quantized_per_channel) ? _gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() : 1;
+
+            CLAuxTensorHandler multipliers(offset_int_vec(Multipliers), _gemm_output_stage_multipliers, tensors, false);
+            CLAuxTensorHandler shifts(offset_int_vec(Shifts), _gemm_output_stage_shifts, tensors, false);
+
+            ICLTensor *multiplier_tensor = multipliers.get();
+            if(multiplier_tensor != nullptr && multiplier_tensor->info()->total_size() > 0)
+            {
+                multiplier_tensor->map(CLScheduler::get().queue(), true);
+                std::memcpy(multiplier_tensor->ptr_to_element(Coordinates(0)), _gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.data(), num_filters * sizeof(int32_t));
+                multiplier_tensor->unmap(CLScheduler::get().queue());
+            }
+
+            ICLTensor *shifts_tensor = shifts.get();
+            if(shifts.get() != nullptr && shifts_tensor->info()->total_size() > 0)
+            {
+                shifts_tensor->map(CLScheduler::get().queue(), true);
+                std::memcpy(shifts_tensor->ptr_to_element(Coordinates(0)), _gemm_info.gemmlowp_output_stage().gemmlowp_shifts.data(), num_filters * sizeof(int32_t));
+                shifts_tensor->unmap(CLScheduler::get().queue());
+            }
+        }
+        _is_prepared = true;
+    }
+    CLScheduler::get().queue().finish();
+}
+
+experimental::MemoryRequirements ClGemmLowpMatrixMultiplyCore::workspace() const
+{
+    return _aux_mem;
+}
+} // namespace opencl
+} // namespace arm_compute
diff --git a/src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h b/src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h
new file mode 100644
index 0000000000..941c169118
--- /dev/null
+++ b/src/runtime/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h
@@ -0,0 +1,155 @@
+/*
+ * Copyright (c) 2017-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CL_GEMMLOWP_MATRIXMULTIPLY_CORE_H
+#define ARM_COMPUTE_CL_GEMMLOWP_MATRIXMULTIPLY_CORE_H
+
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/runtime/CL/CLTypes.h"
+
+#include "src/core/gpu/cl/ClCompileContext.h"
+#include "src/runtime/gpu/cl/IClOperator.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+// Forward declarations
+class ClCastKernel;
+class ClGemmLowpMatrixMultiplyNativeKernel;
+class ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel;
+class ClGemmReshapeRhsMatrixKernel;
+class ClGemmLowpMatrixAReductionKernel;
+class ClGemmLowpMatrixBReductionKernel;
+class ClGemmLowpOffsetContributionKernel;
+class ClGemmLowpOffsetContributionOutputStageKernel;
+} // namespace kernels
+
+/** Basic function to execute GEMMLowpMatrixMultiplyCore on OpenCL. */
+class ClGemmLowpMatrixMultiplyCore : public IClOperator
+{
+public:
+    ClGemmLowpMatrixMultiplyCore();
+    ~ClGemmLowpMatrixMultiplyCore();
+    /** Initialise the kernel's inputs, output
+     *
+     * Valid data layouts:
+     * - NHWC
+     * - NCHW
+     *
+     * Valid data type configurations:
+     * |src0           |src1               |src2     |dst            |
+     * |:--------------|:------------------|:--------|:--------------|
+     * |QASYMM8        |QASYMM8            |S32      |QASYMM8        |
+     * |QASYMM8        |QSYMM8_PER_CHANNEL |S32      |QASYMM8        |
+     * |QASYMM8        |QSYMM8             |S32      |QASYMM8        |
+     * |QASYMM8        |QASYMM8            |S32      |S32            |
+     * |QASYMM8        |QSYMM8_PER_CHANNEL |S32      |S32            |
+     * |QASYMM8        |QSYMM8             |S32      |S32            |
+     * |QASYMM8_SIGNED |QASYMM8_SIGNED     |S32      |QASYMM8_SIGNED |
+     * |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32      |QASYMM8_SIGNED |
+     * |QASYMM8_SIGNED |QSYMM8             |S32      |QASYMM8_SIGNED |
+     * |QASYMM8_SIGNED |QASYMM8_SIGNED     |S32      |S32            |
+     * |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32      |S32            |
+     * |QASYMM8_SIGNED |QSYMM8             |S32      |S32            |
+     *
+     * @note GEMMLowp:  low precision GEMM kernel. [A * B + C]
+     *  This kernel performs the following computations:
+     *
+     *  -# Convert a values from 8-bit quantized to int32 and add a_offset to each of them.
+     *  -# Convert b values from 8-bit quantized to int32 and add b_offset to each of them.
+     *  -# Compute the matrix product of the resulting a * b in int32.
+     *  -# Quantize to uint8 if gemm_info.gemmlowp_output_stage != NONE
+     *
+     * @param[in]  compile_context The compile context to be used.
+     * @param[in]  a               First input tensor  (Matrix A). Data type supported: QASYMM8/QASYMM8_SIGNED.
+     * @param[in]  b               Second input tensor (Matrix B). Data type supported: same as @p a
+     * @param[in]  c               Third input tensor  (Matrix C). It can be a nullptr. Data type supported: S32
+     * @param[out] output          Output tensor. Data type supported: S32 or QASYMM8/QASYMM8_SIGNED if gemm_info.gemmlowp_output_stage != NONE
+     * @param[in]  gemm_info       (Optional) Specifies if the matrix A and/or matrix B have been reshaped and
+     *                       if the reshape of matrix B should be executed only for the first run
+     */
+    void configure(const CLCompileContext &compile_context, ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, const GEMMInfo &gemm_info = GEMMInfo());
+    /** Static function to check if given info will lead to a valid configuration
+     *
+     * Similar to ClGemmLowpMatrixMultiplyCore::configure()
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info = GEMMInfo());
+
+    // Inherited methods overridden:
+    void run(ITensorPack &tensors) override;
+    void prepare(ITensorPack &constants) override;
+    experimental::MemoryRequirements workspace() const override;
+
+private:
+    enum AuxTensorIdx
+    {
+        VecSumCol = 0,
+        VecSumRow,
+        RhsQAsymm8,
+        RhsReshape,
+        ResultS32,
+        Multipliers,
+        Shifts,
+        Count
+    };
+
+private:
+    // Kernels used
+    std::unique_ptr<kernels::ClCastKernel>                                  _weights_to_qasymm8;
+    std::unique_ptr<kernels::ClGemmLowpMatrixMultiplyNativeKernel>          _mm_native_kernel;
+    std::unique_ptr<kernels::ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel> _mm_reshaped_only_rhs_kernel;
+    std::unique_ptr<kernels::ClGemmReshapeRhsMatrixKernel>                  _mtx_b_reshape_kernel;
+    std::unique_ptr<kernels::ClGemmLowpMatrixAReductionKernel>              _mtx_a_reduction_kernel;
+    std::unique_ptr<kernels::ClGemmLowpMatrixBReductionKernel>              _mtx_b_reduction_kernel;
+    std::unique_ptr<kernels::ClGemmLowpOffsetContributionKernel>            _offset_contribution_kernel;
+    std::unique_ptr<kernels::ClGemmLowpOffsetContributionOutputStageKernel> _offset_contribution_output_stage_kernel;
+
+    // Temporary tensors
+    TensorInfo _qasymm8_weights{};
+    TensorInfo _vector_sum_col{};
+    TensorInfo _vector_sum_row{};
+    TensorInfo _tmp_b{};
+    TensorInfo _mm_result_s32{};
+    TensorInfo _gemm_output_stage_multipliers{};
+    TensorInfo _gemm_output_stage_shifts{};
+
+    int32_t  _a_offset{ 0 };
+    int32_t  _b_offset{ 0 };
+    bool     _is_gemm_reshaped{ true };
+    bool     _reshape_b_only_on_first_run{ false };
+    bool     _run_output_stage{ false };
+    bool     _convert_to_qasymm8{ false };
+    bool     _run_offset_contribution{ false };
+    bool     _is_prepared{ false };
+    GEMMInfo _gemm_info{};
+
+    experimental::MemoryRequirements _aux_mem{};
+};
+} // namespace opencl
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CL_GEMMLOWP_MATRIXMULTIPLY_CORE_H */
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