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diff --git a/src/cpu/operators/CpuMatMul.cpp b/src/cpu/operators/CpuMatMul.cpp
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+++ b/src/cpu/operators/CpuMatMul.cpp
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+/*
+ * Copyright (c) 2023 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/cpu/operators/CpuMatMul.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/experimental/Types.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/NEON/NEScheduler.h"
+#include "arm_compute/runtime/NEON/functions/NEMatMul.h"
+#include "src/common/utils/Log.h"
+#include "src/core/CPP/Validate.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/MemoryHelpers.h"
+#include "src/cpu/utils/CpuAuxTensorHandler.h"
+
+using namespace arm_compute::experimental;
+
+namespace arm_compute
+{
+namespace cpu
+{
+CpuMatMul::CpuMatMul()
+    : _transpose_kernel_lhs(), _transpose_kernel_rhs(), _asm_glue(), _lhs_transposed(), _rhs_transposed(), _original_lhs_shape(), _original_rhs_shape(), _original_dst_shape()
+{
+}
+
+Status CpuMatMul::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulInfo &info, const CpuMatMulSettings &settings)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs->are_values_constant(), "LHS Tensor must be dynamic.");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs->are_values_constant(), "RHS Tensor must be dynamic.");
+    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(lhs);
+    ARM_COMPUTE_RETURN_ERROR_ON_CPU_BF16_UNSUPPORTED(lhs);
+
+    const auto adj_lhs = info.adj_lhs();
+    const auto adj_rhs = info.adj_rhs();
+
+    const ITensorInfo *lhs_to_use = lhs;
+    const ITensorInfo *rhs_to_use = rhs;
+    TensorInfo         lhs_transposed{};
+    TensorInfo         rhs_transposed{};
+
+    auto gemm_info            = AsmGemmInfo();
+    gemm_info.activation_info = info.fused_activation();
+    gemm_info.fast_mode       = settings.fast_math();
+
+    // Validate and then permute a/b
+    if(adj_lhs)
+    {
+        auto_init_if_empty(lhs_transposed, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transposed_shape(*lhs)));
+        ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuTransposeKernel::validate(lhs_to_use, &lhs_transposed));
+        // Assign lhs_to_use pointer to use transposed TensorInfo
+        lhs_to_use = &lhs_transposed;
+    }
+    if(adj_rhs)
+    {
+        auto_init_if_empty(rhs_transposed, rhs->clone()->set_tensor_shape(misc::shape_calculator::compute_transposed_shape(*rhs)));
+        ARM_COMPUTE_RETURN_ON_ERROR(cpu::kernels::CpuTransposeKernel::validate(rhs_to_use, &rhs_transposed));
+        // Assign rhs_to_use pointer to use transposed TensorInfo
+        rhs_to_use = &rhs_transposed;
+    }
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_to_use->dimension(0) != rhs_to_use->dimension(1),
+                                    "The product AB is defined only if the number of columns in A is equal to the number of rows in B (after transpose)");
+
+    if(lhs_to_use->num_dimensions() > 2)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_to_use->num_dimensions() != rhs_to_use->num_dimensions(), "Broadcasting in Batch dimension is unsupported by this operator.");
+    }
+
+    // Iterate over dimensions to be collapsed in operator - check dimensions are equivelent between tensors
+    for(unsigned int i = 2; i < lhs_to_use->num_dimensions(); i++)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_to_use->dimension(i) != rhs_to_use->dimension(i), "Broadcasting in Batch dimension is unsupported by this operator.");
+    }
+
+    cpu::CpuGemmAssemblyDispatch::validate(lhs_to_use, rhs_to_use, nullptr, dst, gemm_info);
+
+    return Status{};
+}
+
+void CpuMatMul::configure(ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulInfo &info, const CpuMatMulSettings &settings)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
+    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, info, settings);
+    ARM_COMPUTE_ERROR_THROW_ON(CpuMatMul::validate(lhs, rhs, dst, info, settings));
+
+    _adj_lhs   = info.adj_lhs();
+    _adj_rhs   = info.adj_rhs();
+    _fast_math = settings.fast_math();
+
+    // 1. Create and reshape tensors
+    // ------------------------------------------------------
+    // a. Clone TensorInfo to prevent changing original tensor values during setup
+    // b. Change shape of lhs/dst to [x, y, 1, collapsed(z)] to match assembly kernel configuration
+    // c. For rhs collapse all dimensions larger than 3 to z dimension
+    TensorInfo lhs_to_use = *lhs->clone();
+    TensorInfo dst_to_use = *dst->clone();
+    TensorInfo rhs_to_use = *rhs->clone();
+
+    // Save starting shape of tensors
+    _original_lhs_shape = lhs_to_use.tensor_shape();
+    _original_dst_shape = dst_to_use.tensor_shape();
+    _original_rhs_shape = rhs_to_use.tensor_shape();
+
+    // Reshape lhs for use with assembly kernels.
+    lhs_to_use.set_tensor_shape(TensorShape(_original_lhs_shape.x(), _original_lhs_shape.y(), 1, _original_lhs_shape.collapsed_from(2).z()));
+    dst_to_use.set_tensor_shape(TensorShape(_original_dst_shape.x(), _original_dst_shape.y(), 1, _original_dst_shape.collapsed_from(2).z()));
+    rhs_to_use.set_tensor_shape(_original_rhs_shape.collapsed_from(2));
+
+    // 2.  Configuration for transpose of lhs/rhs
+    // ------------------------------------------------------
+    // Initialise transposed TensorInfo class for aux tensors (intermediary tensors)
+    if(_adj_lhs)
+    {
+        // Setup transpose LHS
+        _transpose_kernel_lhs = std::make_unique<cpu::kernels::CpuTransposeKernel>();
+        _transpose_kernel_lhs->configure(&lhs_to_use, &_lhs_transposed);
+    }
+
+    if(_adj_rhs)
+    {
+        // Setup transpose RHS
+        _transpose_kernel_rhs = std::make_unique<cpu::kernels::CpuTransposeKernel>();
+        _transpose_kernel_rhs->configure(&rhs_to_use, &_rhs_transposed);
+    }
+
+    // 3. Configure assembly kernel using transposed tensors.
+    // -----------------------------------------------------
+    // Use transposed tensors if the corresponding transpose flags are set
+    // Fill AsmGemmInfo class object before configuration
+    _gemm_info.activation_info = info.fused_activation();
+    _gemm_info.fast_mode       = settings.fast_math();
+
+    lhs_to_use = (_adj_lhs) ? _lhs_transposed : lhs_to_use;
+    rhs_to_use = (_adj_rhs) ? _rhs_transposed : rhs_to_use;
+
+    // Configure Asm Kernel
+    _asm_glue = std::make_unique<cpu::CpuGemmAssemblyDispatch>();
+    _asm_glue->configure(&lhs_to_use, &rhs_to_use, nullptr, &dst_to_use, _gemm_info); // c is nullptr as bias not supported in MatMul
+
+    // Specify memory requirements for intermediate tensors
+    auto asm_mem_req = _asm_glue->workspace();
+    // Specify memory required by gemm kernel
+    int idx = 0;
+    for(const auto &aux : asm_mem_req)
+    {
+        _aux_mem[idx] = aux;
+        idx++;
+    }
+    // Memory requirements for transposed tensors
+    _aux_mem[TransposeLHS] = MemoryInfo(offset_int_vec(TransposeLHS), MemoryLifetime::Temporary, lhs->total_size());
+    _aux_mem[TransposeRHS] = MemoryInfo(offset_int_vec(TransposeRHS), MemoryLifetime::Temporary, rhs->total_size());
+}
+
+void CpuMatMul::run(ITensorPack &tensors)
+{
+    // Retrieve tensors from tensor pack
+    auto lhs = tensors.get_tensor(ACL_SRC_0);
+    auto rhs = tensors.get_const_tensor(ACL_SRC_1);
+    auto dst = tensors.get_tensor(ACL_DST);
+
+    // Reshape LHS and DST to ensure compatibility with GEMM asm kernel (Batch dimensions is 4th for lhs and dst within asm)
+    // Collapse RHS (necessary to support dimensions larger than 3 in gemm assembly)
+    lhs->info()->set_tensor_shape(TensorShape(_original_lhs_shape.x(), _original_lhs_shape.y(), 1, _original_lhs_shape.collapsed_from(2).z())); // Collapsed 3+ dimensions into z
+    dst->info()->set_tensor_shape(TensorShape(_original_dst_shape.x(), _original_dst_shape.y(), 1, _original_dst_shape.collapsed_from(2).z())); // Collapsed 3+ dimensions into z
+    rhs->info()->set_tensor_shape(_original_rhs_shape.collapsed_from(2));
+
+    // Initialise object to handle stored transposed tensors in auxillary memory
+    CpuAuxTensorHandler lhs_transposed(offset_int_vec(TransposeLHS), _lhs_transposed, tensors, true);
+    CpuAuxTensorHandler rhs_transposed(offset_int_vec(TransposeRHS), _rhs_transposed, tensors, true);
+
+    // Create tensor pack for asm kernel
+    ITensorPack asm_tensors(tensors);
+
+    // Run transpose lhs if necessary
+    if(_adj_lhs)
+    {
+        ITensorPack lhs_transpose_pack = { { TensorType::ACL_SRC, lhs }, { TensorType::ACL_DST, lhs_transposed.get() } };
+        NEScheduler::get().schedule_op(_transpose_kernel_lhs.get(), Window::DimY, _transpose_kernel_lhs->window(), lhs_transpose_pack);
+        asm_tensors.add_const_tensor(TensorType::ACL_SRC_0, lhs_transposed.get());
+    }
+    // Run transpose rhs if necessary
+    if(_adj_rhs)
+    {
+        ITensorPack rhs_transpose_pack = { { TensorType::ACL_SRC, rhs }, { TensorType::ACL_DST, rhs_transposed.get() } };
+        NEScheduler::get().schedule_op(_transpose_kernel_rhs.get(), Window::DimY, _transpose_kernel_rhs->window(), rhs_transpose_pack);
+        asm_tensors.add_const_tensor(TensorType::ACL_SRC_1, rhs_transposed.get());
+    }
+    // Run asm kernel
+    _asm_glue->run(asm_tensors);
+
+    // Undo reshape of tensors
+    dst->info()->set_tensor_shape(_original_dst_shape);
+    lhs->info()->set_tensor_shape(_original_lhs_shape);
+    rhs->info()->set_tensor_shape(_original_rhs_shape);
+}
+
+experimental::MemoryRequirements CpuMatMul::workspace() const
+{
+    return _aux_mem;
+}
+} // namespace cpu
+} // namespace arm_compute