diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
index f51d0f9..19f38bf 100644
--- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp
@@ -95,7 +95,7 @@
         // Create kernels according to the architecture, data type and input size.
         if(gpu_target == GPUTarget::BIFROST && data_type == DataType::F32)
         {
-            num_elems_processed_per_iteration_x = (input1->dimension(0) <= 1000) ? 2 : 4;
+            num_elems_processed_per_iteration_x = (input1->dimension(0) <= 1000 && input0->num_dimensions() == 1) ? 2 : 4;
         }
 
         // Configure window
@@ -196,7 +196,7 @@
             // The first kernel is optimized for the case of 1000 or less output elements (e.g. FC8 of AlexNet and VGG-16, and
             // FC1 of Inception v3). The second kernel is optimized for the case of greater than 1000 output elements (e.g.
             // FC6 and FC7 of AlexNet and VGG-16).
-            kernel_name = (input1->info()->dimension(0) <= 1000) ? "gemm_mm_floating_point_f32_bifrost_1000" : "gemm_mm_floating_point_f32_bifrost";
+            kernel_name = (input1->info()->dimension(0) <= 1000 && input0->info()->num_dimensions() == 1) ? "gemm_mm_floating_point_f32_bifrost_1000" : "gemm_mm_floating_point_f32_bifrost";
 
             // The work-group size equal to the Bifrost quad size has been proved to be optimal for these kernels
             // via exhaustive autotuning over a range of representative layer configurations.
diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp
index 64c31d5..2c1ddc3 100644
--- a/src/runtime/CL/functions/CLConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp
@@ -43,9 +43,6 @@
 
 void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW)
 {
-    ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output);
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
 
     if(biases != nullptr)
@@ -82,6 +79,8 @@
     {
         _weights_reshape_kernel.configure(weights, biases_to_use, output);
     }
+
+    output->info()->set_quantization_info(weights->info()->quantization_info());
 }
 
 void CLConvolutionLayerReshapeWeights::run()
@@ -100,8 +99,8 @@
 
 CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(memory_manager), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _output_col2im_kernel(),
-      _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false),
-      _are_weights_reshaped(false), _is_quantized(false)
+      _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _are_weights_reshaped(false), _is_quantized(false),
+      _is_interleaved_transposed(false)
 {
 }
 
@@ -157,14 +156,16 @@
 
     const DataType dt = input->info()->data_type();
 
-    // Set the GPU target for matrix multiply
+    // Set the GPU target for matrix multiply and im2col and col2im
     _mm_kernel.set_target(CLScheduler::get().target());
+    _input_im2col_kernel.set_target(CLScheduler::get().target());
+    _output_col2im_kernel.set_target(CLScheduler::get().target());
 
-    _append_bias          = (biases != nullptr) && (!_is_quantized);
-    _are_weights_reshaped = weights_info.are_reshaped();
+    const bool append_bias = (biases != nullptr) && (!_is_quantized);
+    _are_weights_reshaped  = weights_info.are_reshaped();
 
-    const unsigned   bias_element  = (_append_bias) ? 1 : 0;
-    const ICLTensor *biases_to_use = (_append_bias) ? biases : nullptr;
+    const unsigned   bias_element  = (append_bias) ? 1 : 0;
+    const ICLTensor *biases_to_use = (append_bias) ? biases : nullptr;
 
     // Get parameters from conv_info
     unsigned int stride_x = 0;
@@ -181,8 +182,8 @@
                                                  conv_info);
 
     // Check if its a "fully connected" convolution
-    _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
-    const bool run_interleaved      = (!_is_fully_connected_convolution && !_is_quantized);
+    const bool is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
+    _is_interleaved_transposed                = (!is_fully_connected_convolution && !_is_quantized);
 
     unsigned int mat_weights_cols = weights->info()->dimension(3);
     unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element;
@@ -190,7 +191,7 @@
     // Reshape weights if needed
     if(_are_weights_reshaped)
     {
-        if(_is_fully_connected_convolution || _is_quantized)
+        if(is_fully_connected_convolution || _is_quantized)
         {
             mat_weights_cols = weights->info()->dimension(0);
             mat_weights_rows = weights->info()->dimension(1);
@@ -204,22 +205,9 @@
     }
     else
     {
-        if(_is_fully_connected_convolution || _is_quantized)
-        {
-            // Create tensor to store the reshaped weights
-            TensorShape shape_wr(mat_weights_cols, mat_weights_rows);
-            _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr));
-            _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */);
-        }
-        else
-        {
-            // Create tensor to store transposed weights
-            const float transpose_width = 16.0f / input->info()->element_size();
-            TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width)));
-            _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt));
-            _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */);
-        }
-        _weights_reshaped.info()->set_quantization_info(weights->info()->quantization_info());
+        // _weights_reshaped will be auto configured in the kernel
+        _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved_transposed /* 1xW transpose */);
+
         weights = &_weights_reshaped;
     }
 
@@ -236,19 +224,6 @@
     _input_im2col_reshaped.allocator()->init(im2col_reshaped_info);
     _memory_group.manage(&_input_im2col_reshaped);
 
-    // Create tensor (interleave) to prepare input tensor for GEMM
-    if(run_interleaved)
-    {
-        TensorShape shape_interleaved = shape_im2col;
-        shape_interleaved.set(0, shape_interleaved.x() * 4);
-        shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
-        // FIXME: input->clone() doesn't work with subtensors for grouped convolutions.
-        TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position());
-        interleaved_info.set_quantization_info(input->info()->quantization_info());
-        _input_interleaved_reshaped.allocator()->init(interleaved_info);
-        _memory_group.manage(&_input_interleaved_reshaped);
-    }
-
     // Create GEMM output tensor
     TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape();
     shape_gemm.set(0, mat_weights_cols);
@@ -261,14 +236,17 @@
     _gemm_output.allocator()->init(info_gemm);
     _memory_group.manage(&_gemm_output);
 
-    // Configure kernels
-    _input_im2col_kernel.set_target(CLScheduler::get().target());
-    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias);
+    // Configure im2col
+    _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, append_bias);
 
     // Configure matrix multiply
-    if(run_interleaved)
+    if(_is_interleaved_transposed)
     {
+        // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
         _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
+        _memory_group.manage(&_input_interleaved_reshaped);
+
+        // Configure GEMM
         configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
         _input_interleaved_reshaped.allocator()->allocate();
     }
@@ -289,7 +267,6 @@
     }
 
     // Configure Col2Im
-    _output_col2im_kernel.set_target(CLScheduler::get().target());
     _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h));
     if(_is_quantized)
     {
@@ -323,7 +300,7 @@
     // Run im2col
     CLScheduler::get().enqueue(_input_im2col_kernel);
 
-    if(!_is_fully_connected_convolution && !_is_quantized)
+    if(_is_interleaved_transposed)
     {
         // Run interleave4x4
         CLScheduler::get().enqueue(_input_interleave_kernel);
diff --git a/src/runtime/CL/functions/CLGEMM.cpp b/src/runtime/CL/functions/CLGEMM.cpp
index ca0228f..be2527f 100644
--- a/src/runtime/CL/functions/CLGEMM.cpp
+++ b/src/runtime/CL/functions/CLGEMM.cpp
@@ -39,14 +39,17 @@
 using namespace arm_compute;
 
 CLGEMM::CLGEMM(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false)
+    : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false),
+      _is_first_run(true), _reshape_b_only_on_first_run(false)
 {
 }
 
-void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta)
+void CLGEMM::configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, float alpha, float beta, const GEMMInfo &gemm_info)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output);
+    ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
+    ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
 
     if(c != nullptr)
     {
@@ -60,7 +63,11 @@
     ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(0) != b->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
 
     // If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors
-    _is_interleaved_transposed = a->info()->dimension(1) > 16;
+    // For Bifrost architectures we do not reshape the input matrices
+    _is_interleaved_transposed = (a->info()->dimension(1) > 16 && CLScheduler::get().target() != GPUTarget::BIFROST);
+
+    // 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();
 
     const ICLTensor *matrix_a = a;
     const ICLTensor *matrix_b = b;
@@ -73,31 +80,17 @@
         matrix_a = &_tmp_a;
         matrix_b = &_tmp_b;
 
-        TensorShape shape_tmp_a = a->info()->tensor_shape();
-        TensorShape shape_tmp_b = b->info()->tensor_shape();
-
-        shape_tmp_a.set(0, a->info()->dimension(0) * 4);
-        shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f));
-
-        const unsigned int transpose_w = max_cl_vector_width / data_size_from_type(b->info()->data_type());
-        shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w);
-        shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast<float>(transpose_w)));
-
-        TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type(), a->info()->fixed_point_position());
-        _tmp_a.allocator()->init(info_a);
-
-        TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type(), b->info()->fixed_point_position());
-        _tmp_b.allocator()->init(info_b);
-
-        // Manage intermediate buffers
-        _memory_group.manage(&_tmp_a);
-        _memory_group.manage(&_tmp_b);
+        // _tmp_a and _tmp_n will be auto configured in _interleave_kernel and in _transpose_kernel
 
         // Configure interleave kernel
         _interleave_kernel.configure(a, &_tmp_a);
 
         // Configure transpose kernel
         _transpose_kernel.configure(b, &_tmp_b);
+
+        // Manage intermediate buffers
+        _memory_group.manage(&_tmp_a);
+        _memory_group.manage(&_tmp_b);
     }
 
     _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed);
@@ -126,8 +119,18 @@
         // Run interleave kernel
         CLScheduler::get().enqueue(_interleave_kernel, false);
 
-        // Run transpose kernel
-        CLScheduler::get().enqueue(_transpose_kernel, false);
+        if(_is_first_run)
+        {
+            // Run transpose kernel
+            CLScheduler::get().enqueue(_transpose_kernel, false);
+
+            _is_first_run = false;
+        }
+        else if(!_reshape_b_only_on_first_run)
+        {
+            // Run transpose kernel
+            CLScheduler::get().enqueue(_transpose_kernel, false);
+        }
     }
 
     // Run matrix multiply kernel
diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp
index 03ba43f..e640b06 100644
--- a/src/runtime/NEON/functions/NEGEMM.cpp
+++ b/src/runtime/NEON/functions/NEGEMM.cpp
@@ -50,15 +50,17 @@
 {
 NEGEMM::NEGEMM(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _mm_optimised_kernel(nullptr), _ma_kernel(), _tmp_a(), _tmp_b(), _workspace(),
-      _run_vector_matrix_multiplication(false), _run_addition(false)
+      _run_vector_matrix_multiplication(false), _run_addition(false), _is_first_run(true), _reshape_b_only_on_first_run(false)
 {
 }
 
-void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta)
+void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *d, float alpha, float beta, const GEMMInfo &gemm_info)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32, DataType::F16, DataType::QS8, DataType::QS16);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, d);
     ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(0) != b->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B");
+    ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported");
+    ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported");
 
     if(c != nullptr)
     {
@@ -70,6 +72,8 @@
         ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(1) != d->info()->dimension(1), "The C matrix must have the same number of columns as the output matrix");
     }
 
+    // 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();
     _run_vector_matrix_multiplication = a->info()->dimension(1) < 2;
 
     // Check if the first input tensor is a vector.
@@ -207,8 +211,18 @@
             // Run interleave kernel
             NEScheduler::get().schedule(&_interleave_kernel, Window::DimY);
 
-            // Run transpose kernel
-            NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
+            if(_is_first_run)
+            {
+                // Run transpose kernel
+                NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
+
+                _is_first_run = false;
+            }
+            else if(!_reshape_b_only_on_first_run)
+            {
+                // Run transpose kernel
+                NEScheduler::get().schedule(&_transpose_kernel, Window::DimY);
+            }
         }
 
         NEScheduler::get().schedule(&_mm_kernel, _run_vector_matrix_multiplication ? Window::DimX : Window::DimY);