diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index cf1a82b..1bc785a 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -23,646 +23,48 @@
  */
 #include "arm_compute/runtime/CL/functions/CLGEMM.h"
 
+#include "arm_compute/core/CL/CLHelpers.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/GPUTarget.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/Utils.h"
-#include "arm_compute/core/Validate.h"
-#include "arm_compute/core/utils/misc/ShapeCalculator.h"
-#include "arm_compute/runtime/CL/CLScheduler.h"
-#include "arm_compute/runtime/ITensorAllocator.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
-#include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
-#include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/utils/helpers/float_ops.h"
-#include "src/runtime/CL/gemm/CLGEMMKernelSelection.h"
-#include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h"
-#include "support/Cast.h"
-#include "utils/TypePrinter.h"
+#include "arm_compute/runtime/CL/functions/CLGEMM.h"
+#include "src/core/helpers/MemoryHelpers.h"
+#include "src/runtime/gpu/cl/operators/ClGemm.h"
 
 namespace arm_compute
 {
-using namespace arm_compute::misc::shape_calculator;
-using namespace arm_compute::cl_gemm;
-using namespace arm_compute::utils::cast;
+using namespace arm_compute::experimental;
+using OperatorType = opencl::ClGemm;
 
-namespace weights_transformations
+struct CLGEMM::Impl
 {
-CLGEMMReshapeRHSMatrixKernelManaged::CLGEMMReshapeRHSMatrixKernelManaged()
-    : _kernel(std::make_unique<CLGEMMReshapeRHSMatrixKernel>())
-{
-}
-
-CLGEMMReshapeRHSMatrixKernelManaged::~CLGEMMReshapeRHSMatrixKernelManaged() = default;
-
-void CLGEMMReshapeRHSMatrixKernelManaged::run()
-{
-    _output.allocator()->allocate();
-    CLScheduler::get().enqueue(*_kernel, false);
-    _reshape_run = true;
-}
-
-void CLGEMMReshapeRHSMatrixKernelManaged::release()
-{
-    _output.allocator()->free();
-}
-
-ICLTensor *CLGEMMReshapeRHSMatrixKernelManaged::get_weights()
-{
-    return &_output;
-}
-
-uint32_t CLGEMMReshapeRHSMatrixKernelManaged::uid()
-{
-    return _uid;
-}
-
-void CLGEMMReshapeRHSMatrixKernelManaged::configure(const ICLTensor *input, GEMMRHSMatrixInfo info)
-{
-    configure(CLKernelLibrary::get().get_compile_context(), input, info);
-}
-
-void CLGEMMReshapeRHSMatrixKernelManaged::configure(const CLCompileContext &compile_context, const ICLTensor *input, GEMMRHSMatrixInfo info)
-{
-    _kernel->configure(compile_context, input, &_output, info);
-}
-} // namespace weights_transformations
-
-namespace
-{
-inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type)
-{
-    switch(kernel_type)
-    {
-        case CLGEMMKernelType::NATIVE_V1:
-        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
-        case CLGEMMKernelType::RESHAPED_V1:
-        case CLGEMMKernelType::RESHAPED:
-        {
-            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 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 *c,
-                                                    const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info)
-{
-    // 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
-    gemm_kernel_info.lhs_info  = lhs_info;
-    gemm_kernel_info.rhs_info  = rhs_info;
-    gemm_kernel_info.has_pad_y = false;
-    if(!bool(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info)))
-    {
-        return false;
-    }
-    gemm_kernel_info.has_pad_y = true;
-    if(!bool(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info)))
-    {
-        return false;
-    }
-    return true;
-}
-
-//Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs
-inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a,
-                                                                                                 const ITensorInfo *b,
-                                                                                                 const ITensorInfo *c, 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, c, output, kernel_info))
-        {
-            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 };
-}
-
-// Validate lhs_info and rhs_info for reshaped kernel
-inline bool validate_lhs_rhs_info_reshaped(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c,
-                                           const ITensorInfo *output, GEMMKernelInfo gemm_kernel_info, bool reinterpret_input_as_3d)
-{
-    // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped kernel
-    TensorInfo tmp_a_info{};
-    TensorInfo tmp_b_info{};
-
-    // Validate reshape LHS kernel
-    auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, reinterpret_input_as_3d)));
-    if(!bool(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, reinterpret_input_as_3d)))
-    {
-        return false;
-    }
-
-    // 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
-    gemm_kernel_info.lhs_info = lhs_info;
-    gemm_kernel_info.rhs_info = rhs_info;
-    if(!bool(CLGEMMMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, 1.f, 0.f, lhs_info, rhs_info, gemm_kernel_info)))
-    {
-        return false;
-    }
-    return true;
-}
-
-//Automatically select between mlgo (prioritized) and default heuristics for reshaped kernel configs
-inline std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery query, GEMMKernelInfo kernel_info, const ITensorInfo *a, const ITensorInfo *b,
-                                                                                        const ITensorInfo *c, const ITensorInfo *output, bool reinterpret_input_as_3d)
-{
-    auto config = auto_heuristics::select_mlgo_gemm_config_reshaped(query);
-    if(config)
-    {
-        if(validate_lhs_rhs_info_reshaped(config.lhs_info, config.rhs_info, a, b, c, output, kernel_info, reinterpret_input_as_3d))
-        {
-            ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped 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(query);
-    ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped 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 };
-}
-
-} // namespace
+    const ICLTensor              *a{ nullptr };
+    const ICLTensor              *b{ nullptr };
+    const ICLTensor              *c{ nullptr };
+    ICLTensor                    *dst{ nullptr };
+    std::unique_ptr<OperatorType> op{ nullptr };
+    MemoryGroup                   memory_group{};
+    IWeightsManager              *weights_manager{ nullptr };
+    CLTensor                      weights_transformed{};
+    ITensorPack                   run_pack{};
+    ITensorPack                   prep_pack{};
+    MemoryRequirements            aux_mem_req{};
+    WorkspaceData<CLTensor>       workspace_tensors{};
+    bool                          _is_prepared{ false };
+};
 
 CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager, IWeightsManager *weights_manager)
-    : _memory_group(std::move(memory_manager)),
-      _weights_manager(weights_manager),
-      _mm_kernel(std::make_unique<CLGEMMMatrixMultiplyKernel>()),
-      _reshape_lhs_kernel(std::make_unique<CLGEMMReshapeLHSMatrixKernel>()),
-      _reshape_rhs_kernel(std::make_unique<CLGEMMReshapeRHSMatrixKernel>()),
-      _reshape_rhs_kernel_managed(std::make_unique<weights_transformations::CLGEMMReshapeRHSMatrixKernelManaged>()),
-      _mm_reshaped_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedKernel>()),
-      _mm_reshaped_only_rhs_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>()),
-      _mm_reshaped_only_rhs_fallback_kernel(std::make_unique<CLGEMMMatrixMultiplyReshapedOnlyRHSKernel>()),
-      _tmp_a(),
-      _tmp_b(),
-      _original_b(nullptr),
-      _lhs(nullptr),
-      _dst(nullptr),
-      _reshape_b_only_on_first_run(false),
-      _is_prepared(false),
-      _gemm_kernel_type(CLGEMMKernelType::NATIVE_V1)
+    : _impl(std::make_unique<Impl>())
 {
+    _impl->memory_group    = MemoryGroup(memory_manager);
+    _impl->weights_manager = weights_manager;
 }
 
 CLGEMM::~CLGEMM() = default;
 
-void CLGEMM::configure_native_v1(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta,
-                                 const GEMMInfo &gemm_info)
-{
-    const unsigned int m          = gemm_info.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 GPUTarget    gpu_target = CLScheduler::get().target();
-
-    // Set the target for the kernels
-    _mm_kernel->set_target(gpu_target);
-
-    GEMMReshapeInfo reshape_info(m, n, k, 1, 1, gemm_info.depth_output_gemm3d(), gemm_info.reinterpret_input_as_3d(), gemm_info.broadcast_bias());
-
-    // Configure and tune matrix multiply kernel
-    _mm_kernel->configure(compile_context, a, b, c, output, alpha, beta, false, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info());
-
-    // Tune kernel statically
-    CLScheduler::get().tune_kernel_static(*_mm_kernel);
-}
-
-void CLGEMM::configure_reshaped_v1(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta,
-                                   const GEMMInfo &gemm_info)
-{
-    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 int          depth_output_gemm3d       = gemm_info.depth_output_gemm3d();
-    const GPUTarget    gpu_target                = CLScheduler::get().target();
-    int                mult_transpose1xW_width   = 1;
-    int                mult_interleave4x4_height = 1;
-
-    // Set the target for the kernels
-    _reshape_lhs_kernel->set_target(gpu_target);
-    _mm_kernel->set_target(gpu_target);
-
-    if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
-    {
-        mult_transpose1xW_width   = 4;
-        mult_interleave4x4_height = 2;
-    }
-
-    GEMMRHSMatrixInfo rhs_info;
-    rhs_info.n0         = 16 / b->info()->element_size();
-    rhs_info.k0         = 1;
-    rhs_info.h0         = mult_transpose1xW_width;
-    rhs_info.interleave = false;
-    rhs_info.transpose  = false;
-
-    GEMMLHSMatrixInfo lhs_info;
-    lhs_info.m0         = 4;
-    lhs_info.k0         = 4;
-    lhs_info.v0         = mult_interleave4x4_height;
-    lhs_info.interleave = true;
-    lhs_info.transpose  = true;
-
-    GEMMReshapeInfo reshape_info(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias());
-
-    const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b));
-
-    // Manage intermediate buffers
-    _memory_group.manage(&_tmp_a);
-
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _memory_group.manage(&_tmp_b);
-    }
-
-    // Configure interleave kernel
-    _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, reinterpret_input_as_3d);
-
-    // Configure transpose kernel
-    ICLTensor *reshaped_rhs = &_tmp_b;
-    if(_weights_manager && _weights_manager->are_weights_managed(b))
-    {
-        _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info);
-        reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get()));
-    }
-    else
-    {
-        _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info);
-    }
-
-    // Configure and tune matrix multiply kernel
-    _mm_kernel->configure(compile_context, &_tmp_a, reshaped_rhs, c, output, alpha, beta, true, reshape_info, gemm_info.fp_mixed_precision(), gemm_info.activation_info());
-
-    CLScheduler::get().tune_kernel_static(*_mm_kernel);
-
-    // Allocate intermediate tensors
-    _tmp_a.allocator()->allocate();
-
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _tmp_b.allocator()->allocate();
-    }
-}
-
-void CLGEMM::configure_reshaped_v2(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta,
-                                   const GEMMInfo &gemm_info)
-{
-    DataType           data_type               = a->info()->data_type();
-    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 GPUTarget    gpu_target              = CLScheduler::get().target();
-    bool               broadcast_bias          = gemm_info.broadcast_bias();
-
-    GEMMKernelInfo kernel_info;
-    kernel_info.m                       = m;
-    kernel_info.n                       = n;
-    kernel_info.k                       = k;
-    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    kernel_info.reinterpret_input_as_3d = false;
-    kernel_info.broadcast_bias          = broadcast_bias;
-    kernel_info.activation_info         = gemm_info.activation_info();
-
-    // Set the target for the kernels
-    _reshape_lhs_kernel->set_target(gpu_target);
-    _mm_kernel->set_target(gpu_target);
-
-    const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b));
-
-    // Manage intermediate buffers
-    _memory_group.manage(&_tmp_a);
-
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _memory_group.manage(&_tmp_b);
-    }
-
-    // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel
-
-    GEMMLHSMatrixInfo lhs_info{};
-    GEMMRHSMatrixInfo rhs_info{};
-
-    // Pick up the GEMM configuration
-    std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a->info(), b->info(),
-                                                                    c == nullptr ? nullptr : c->info(), output->info(), gemm_info.reinterpret_input_as_3d());
-
-    _reshape_lhs_kernel->configure(compile_context, a, &_tmp_a, lhs_info, gemm_info.reinterpret_input_as_3d());
-
-    ICLTensor *reshaped_rhs = &_tmp_b;
-    if(_weights_manager && _weights_manager->are_weights_managed(b))
-    {
-        _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info);
-        reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get()));
-    }
-    else
-    {
-        _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info);
-    }
-
-    // Configure and tune matrix multiply kernel
-    _mm_reshaped_kernel->configure(compile_context, &_tmp_a, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info);
-
-    // Allocate intermediate tensors
-    _tmp_a.allocator()->allocate();
-
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _tmp_b.allocator()->allocate();
-    }
-}
-
-void CLGEMM::configure_reshaped_only_rhs(const CLCompileContext &compile_context, const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta,
-                                         const GEMMInfo &gemm_info)
-{
-    DataType           data_type               = a->info()->data_type();
-    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 GPUTarget    gpu_target              = CLScheduler::get().target();
-    bool               broadcast_bias          = gemm_info.broadcast_bias();
-
-    GEMMKernelInfo kernel_info;
-    kernel_info.m                       = m;
-    kernel_info.n                       = n;
-    kernel_info.k                       = k;
-    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
-    kernel_info.broadcast_bias          = broadcast_bias;
-    kernel_info.activation_info         = gemm_info.activation_info();
-
-    // Set the target for the kernels
-    _mm_kernel->set_target(gpu_target);
-
-    const bool use_mm_b = (!_weights_manager || !_weights_manager->are_weights_managed(b));
-
-    // Manage intermediate buffers
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _memory_group.manage(&_tmp_b);
-    }
-
-    GEMMLHSMatrixInfo lhs_info{};
-    GEMMRHSMatrixInfo rhs_info{};
-
-    // Pick up the GEMM configuration
-    std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size }, kernel_info, a->info(), b->info(),
-                                                                             c == nullptr ? nullptr : c->info(), output->info());
-
-    ICLTensor *reshaped_rhs = &_tmp_b;
-    if(_weights_manager && _weights_manager->are_weights_managed(b))
-    {
-        _reshape_rhs_kernel_managed->configure(compile_context, b, rhs_info);
-        reshaped_rhs = utils::cast::polymorphic_downcast<ICLTensor *>(_weights_manager->acquire(b, _reshape_rhs_kernel_managed.get()));
-    }
-    else
-    {
-        _reshape_rhs_kernel->configure(compile_context, b, &_tmp_b, rhs_info);
-    }
-
-    // Configure two variants of CLGEMMMatrixMultiplyReshapedOnlyRHSKernel (has_pad_y = false/true)
-    // During the prepare stage we check the padding requirement for the lhs and dst tensors. If they do not have
-    // pad y, we dispatch CLGEMMMatrixMultiplyReshapedOnlyRHSKernel with has_pad_y = false
-
-    // Configure matrix multiply kernel with no y padding support
-    kernel_info.has_pad_y = false;
-    _mm_reshaped_only_rhs_kernel->configure(compile_context, a, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info);
-
-    // Configure matrix multiply kernel with y padding support
-    kernel_info.has_pad_y = true;
-    _mm_reshaped_only_rhs_fallback_kernel->configure(compile_context, a, reshaped_rhs, c, output, alpha, beta, lhs_info, rhs_info, kernel_info);
-
-    if(!_reshape_b_only_on_first_run && use_mm_b)
-    {
-        _tmp_b.allocator()->allocate();
-    }
-}
-
-Status CLGEMM::validate_native_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
-{
-    ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_UNUSED(output);
-
-    // 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 int          depth_output_gemm3d     = gemm_info.depth_output_gemm3d();
-
-    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d, gemm_info.broadcast_bias());
-
-    // Validate matrix multiply
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(a, b, c, output, alpha, beta,
-                                                                     false, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info()));
-
-    return Status{};
-}
-
-Status CLGEMM::validate_reshaped_v1(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
-{
-    ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_UNUSED(output);
-
-    TensorInfo tmp_a_info{};
-    TensorInfo tmp_b_info{};
-
-    // Get the GPU target
-    const GPUTarget    gpu_target                = CLScheduler::get().target();
-    const unsigned int m                         = gemm_info.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);
-    int                mult_transpose1xW_width   = 1;
-    int                mult_interleave4x4_height = 1;
-    const int          depth_output_gemm3d       = gemm_info.depth_output_gemm3d();
-
-    if(get_arch_from_target(gpu_target) == GPUTarget::BIFROST)
-    {
-        mult_transpose1xW_width   = 4;
-        mult_interleave4x4_height = 2;
-    }
-
-    GEMMRHSMatrixInfo rhs_info;
-    rhs_info.n0         = 16 / b->element_size();
-    rhs_info.k0         = 1;
-    rhs_info.h0         = mult_transpose1xW_width;
-    rhs_info.interleave = false;
-    rhs_info.transpose  = false;
-
-    GEMMLHSMatrixInfo lhs_info;
-    lhs_info.m0         = 4;
-    lhs_info.k0         = 4;
-    lhs_info.v0         = mult_interleave4x4_height;
-    lhs_info.interleave = true;
-    lhs_info.transpose  = true;
-
-    const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height, depth_output_gemm3d, false, gemm_info.broadcast_bias());
-
-    // Validate interleave kernel
-    auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));
-
-    // Validate transpose kernel
-    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));
-
-    // Validate matrix multiply
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta,
-                                                                     true, reshape_info, gpu_target, gemm_info.fp_mixed_precision(), gemm_info.activation_info()));
-
-    return Status{};
-}
-
-Status CLGEMM::validate_reshaped(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
-{
-    ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_UNUSED(output);
-
-    TensorInfo tmp_a_info{};
-    TensorInfo tmp_b_info{};
-
-    // Get the GPU target
-    const GPUTarget    gpu_target              = CLScheduler::get().target();
-    DataType           data_type               = a->data_type();
-    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 bool         broadcast_bias          = gemm_info.broadcast_bias();
-
-    GEMMKernelInfo kernel_info;
-    kernel_info.m                       = m;
-    kernel_info.n                       = n;
-    kernel_info.k                       = k;
-    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    kernel_info.reinterpret_input_as_3d = false;
-    kernel_info.broadcast_bias          = broadcast_bias;
-    kernel_info.activation_info         = gemm_info.activation_info();
-
-    GEMMLHSMatrixInfo lhs_info;
-    GEMMRHSMatrixInfo rhs_info;
-
-    // Pick up the GEMM configuration
-    // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails
-    const auto gemm_config = select_default_gemm_config_reshaped(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size });
-    lhs_info               = gemm_config.lhs_info;
-    rhs_info               = gemm_config.rhs_info;
-
-    auto_init_if_empty(tmp_a_info, a->clone()->set_tensor_shape(compute_lhs_reshaped_shape(*a, lhs_info, gemm_info.reinterpret_input_as_3d())));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeLHSMatrixKernel::validate(a, &tmp_a_info, lhs_info, gemm_info.reinterpret_input_as_3d()));
-
-    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));
-
-    // Validate matrix multiply
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedKernel::validate(&tmp_a_info, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info));
-
-    return Status{};
-}
-
-Status CLGEMM::validate_reshaped_only_rhs(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
-{
-    ARM_COMPUTE_UNUSED(alpha);
-    ARM_COMPUTE_UNUSED(output);
-
-    TensorInfo tmp_b_info{};
-
-    // Get the GPU target
-    const GPUTarget    gpu_target              = CLScheduler::get().target();
-    const DataType     data_type               = a->data_type();
-    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 bool         broadcast_bias          = gemm_info.broadcast_bias();
-
-    GEMMKernelInfo kernel_info;
-    kernel_info.m                       = m;
-    kernel_info.n                       = n;
-    kernel_info.k                       = k;
-    kernel_info.depth_output_gemm3d     = depth_output_gemm3d;
-    kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d;
-    kernel_info.broadcast_bias          = broadcast_bias;
-    kernel_info.activation_info         = gemm_info.activation_info();
-
-    GEMMLHSMatrixInfo lhs_info;
-    GEMMRHSMatrixInfo rhs_info;
-
-    // Pick up the GEMM configuration
-    // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails
-    const auto gemm_config = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, data_type, m, n, k, batch_size });
-    lhs_info               = gemm_config.lhs_info;
-    rhs_info               = gemm_config.rhs_info;
-
-    auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMReshapeRHSMatrixKernel::validate(b, &tmp_b_info, rhs_info));
-
-    // Validate matrix multiply
-    kernel_info.has_pad_y = false;
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info));
-
-    kernel_info.has_pad_y = true;
-    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMMMatrixMultiplyReshapedOnlyRHSKernel::validate(a, &tmp_b_info, c, output, alpha, beta, lhs_info, rhs_info, kernel_info));
-
-    return Status{};
-}
-
 void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
     configure(CLKernelLibrary::get().get_compile_context(), a, b, c, output, alpha, beta, gemm_info);
@@ -672,221 +74,56 @@
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output);
 
-    // Perform validation step
-    ARM_COMPUTE_ERROR_THROW_ON(validate(a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), alpha, beta, gemm_info));
+    _impl->a   = a;
+    _impl->b   = b;
+    _impl->c   = c;
+    _impl->dst = output;
+    _impl->op  = std::make_unique<OperatorType>();
 
-    // Check if we need to reshape the matrix B only on the first run
-    _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run();
-    _is_prepared                 = gemm_info.retain_internal_weights();
-    _original_b                  = b;
-    _lhs                         = a;
-    _dst                         = output;
+    _impl->op->configure(compile_context, a->info(), b->info(), c != nullptr ? c->info() : nullptr, output->info(), alpha, beta, gemm_info);
+    _impl->aux_mem_req = _impl->op->workspace();
 
-    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);
-
-    // Select GEMMType
-    _gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery{ CLScheduler::get().target(), a->info()->data_type(), m, n, k, batch_size }, _reshape_b_only_on_first_run);
-
-    const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr);
-
-    const ICLTensor *c_to_use = fuse_add_c ? c : nullptr;
-
-    switch(_gemm_kernel_type)
-    {
-        case CLGEMMKernelType::NATIVE_V1:
-        {
-            configure_native_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_V1:
-        {
-            configure_reshaped_v1(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED:
-        {
-            configure_reshaped_v2(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
-        {
-            configure_reshaped_only_rhs(compile_context, a, b, c_to_use, output, alpha, beta, gemm_info);
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_ERROR("GEMMType not supported");
-        }
-    }
+    // Manage/allocate auxilairy tensors
+    _impl->run_pack          = { { ACL_SRC_0, _impl->a }, { ACL_SRC_2, _impl->c }, { ACL_DST, _impl->dst } };
+    _impl->prep_pack         = { { ACL_SRC_1, _impl->b } };
+    _impl->workspace_tensors = manage_workspace<CLTensor>(_impl->op->workspace(), _impl->memory_group, _impl->run_pack, _impl->prep_pack);
 }
 
 Status CLGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
-    // Get the GPU 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);
-
-    // Select GEMMType
-    CLGEMMKernelType gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery
-    {
-        CLScheduler::get().target(), a->data_type(), m, n, k, batch_size,
-    },
-    gemm_info.reshape_b_only_on_first_run());
-
-    const bool fuse_add_c = (!(helpers::float_ops::is_zero(beta)) && c != nullptr);
-
-    const ITensorInfo *c_to_use = fuse_add_c ? c : nullptr;
-
-    switch(gemm_kernel_type)
-    {
-        case CLGEMMKernelType::NATIVE_V1:
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_native_v1(a, b, c_to_use, output, alpha, beta, gemm_info));
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_V1:
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_v1(a, b, c_to_use, output, alpha, beta, gemm_info));
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED:
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped(a, b, c_to_use, output, alpha, beta, gemm_info));
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR(validate_reshaped_only_rhs(a, b, c_to_use, output, alpha, beta, gemm_info));
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_RETURN_ERROR_MSG("GEMMType not supported");
-        }
-    }
-
-    return Status{};
+    return OperatorType::validate(a, b, c, output, alpha, beta, gemm_info);
 }
 
 void CLGEMM::run()
 {
     prepare();
-    MemoryGroupResourceScope scope_mg(_memory_group);
 
-    // Run matrix multiply kernel
-    switch(_gemm_kernel_type)
-    {
-        case CLGEMMKernelType::NATIVE_V1:
-        {
-            CLScheduler::get().enqueue(*_mm_kernel, true);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_V1:
-        {
-            // Run interleave kernel
-            CLScheduler::get().enqueue(*_reshape_lhs_kernel, false);
+    MemoryGroupResourceScope scope_mg(_impl->memory_group);
 
-            if(!_reshape_b_only_on_first_run)
-            {
-                // Run transpose kernel
-                if(_weights_manager && _weights_manager->are_weights_managed(_original_b))
-                {
-                    _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get());
-                }
-                else
-                {
-                    CLScheduler::get().enqueue(*_reshape_rhs_kernel, false);
-                }
-            }
-
-            CLScheduler::get().enqueue(*_mm_kernel, true);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED:
-        {
-            // Run interleave kernel
-            CLScheduler::get().enqueue(*_reshape_lhs_kernel, false);
-
-            if(!_reshape_b_only_on_first_run)
-            {
-                // Run transpose kernel
-                if(_weights_manager && _weights_manager->are_weights_managed(_original_b))
-                {
-                    _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get());
-                }
-                else
-                {
-                    CLScheduler::get().enqueue(*_reshape_rhs_kernel, false);
-                }
-            }
-
-            CLScheduler::get().enqueue(*_mm_reshaped_kernel, true);
-            break;
-        }
-        case CLGEMMKernelType::RESHAPED_ONLY_RHS:
-        {
-            if(!_reshape_b_only_on_first_run)
-            {
-                // Run transpose kernel
-                if(_weights_manager && _weights_manager->are_weights_managed(_original_b))
-                {
-                    _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get());
-                }
-                else
-                {
-                    CLScheduler::get().enqueue(*_reshape_rhs_kernel, false);
-                }
-            }
-            // In case of RESHAPED_ONLY_RHS, we need to check the padding requirement
-            // Check if the lhs or dst tensors have padding
-            const unsigned int cross_plane_pad_lhs = _lhs->info()->padding().top + _lhs->info()->padding().bottom;
-            const unsigned int cross_plane_pad_dst = _dst->info()->padding().top + _dst->info()->padding().bottom;
-
-            bool has_pad_y = (cross_plane_pad_lhs != 0) || (cross_plane_pad_dst != 0);
-            if(has_pad_y)
-            {
-                CLScheduler::get().enqueue(*_mm_reshaped_only_rhs_fallback_kernel, true);
-            }
-            else
-            {
-                CLScheduler::get().enqueue(*_mm_reshaped_only_rhs_kernel, true);
-            }
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_ERROR("GEMMType not supported");
-        }
-    }
+    ARM_COMPUTE_ERROR_ON_NULLPTR(_impl->a, _impl->b, _impl->dst);
+    _impl->op->run(_impl->run_pack);
 }
 
 void CLGEMM::prepare()
 {
-    if(!_is_prepared)
+    if(!_impl->_is_prepared)
     {
-        if(_gemm_kernel_type != CLGEMMKernelType::NATIVE_V1 && _reshape_b_only_on_first_run)
+        _impl->op->prepare(_impl->prep_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))
         {
-            if(_weights_manager && _weights_manager->are_weights_managed(_original_b))
-            {
-                _weights_manager->run(_original_b, _reshape_rhs_kernel_managed.get());
-            }
-            else
-            {
-                // Run transpose kernel and mark original weights tensor as unused
-                _tmp_b.allocator()->allocate();
-                CLScheduler::get().enqueue(*_reshape_rhs_kernel, false);
-                _original_b->mark_as_unused();
-            }
+            _impl->b->mark_as_unused();
         }
-        CLScheduler::get().queue().finish();
-        _is_prepared = true;
+        else
+        {
+            // 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);
+        }
+        _impl->_is_prepared = true;
     }
 }
 } // namespace arm_compute