diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
index 99f045a..be6be04 100644
--- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp
@@ -91,22 +91,27 @@
 }
 
 CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _col2im_kernel(), _activationlayer_function(), _add_bias_kernel(),
-      _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _skip_im2col(false), _skip_col2im(false), _is_quantized(false),
-      _is_activationlayer_enabled(false), _is_prepared(false), _run_addition(true)
+    : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _col2im_kernel(), _activationlayer_function(),
+      _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _fuse_activation(true), _is_prepared(false)
 {
 }
 
 void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
-                                          int gemm_3d_depth)
+                                          int gemm_3d_depth, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights);
-    ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), gemmlowp_output_stage, gemm_3d_depth, _skip_im2col,
-                                           _run_addition));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), gemmlowp_output_stage, gemm_3d_depth, _skip_im2col, act_info));
 
-    const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */,
-                                         gemm_3d_depth, _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */,
-                                         false, gemmlowp_output_stage);
+    const GEMMInfo &gemm_info = GEMMInfo(false,                 // is_a_reshaped
+                                         false,                 // is_b_reshaped
+                                         true,                  // reshape_b_only_on_first_run
+                                         gemm_3d_depth,         // depth_output_gemm3d
+                                         _skip_im2col,          // reinterpret_input_as_3d
+                                         false,                 // retain_internal_weights
+                                         gemmlowp_output_stage, // gemmlowp_output_stage
+                                         false,                 // fp_mixed_precision
+                                         true,                  // broadcast_bias
+                                         act_info);             // activation_info
 
     if(_is_quantized)
     {
@@ -126,21 +131,26 @@
     }
     else
     {
-        // Bias does not need to be added in GEMM if im2col is being used or the Matrix Addition kernel needs to be run
-        const bool skip_bias_in_gemm = _run_addition || !_skip_im2col;
         // Configure matrix multiply function
-        _mm_gemm.configure(input, weights, (skip_bias_in_gemm) ? nullptr : biases, output, 1.0f, 1.0f, gemm_info);
+        _mm_gemm.configure(input, weights, biases, output, 1.0f, 1.0f, gemm_info);
     }
 }
 
 Status CLGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
-                                           const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth, bool skip_im2col, bool run_addition)
+                                           const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth, bool skip_im2col, const ActivationLayerInfo &act_info)
 {
     const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
 
-    const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */,
-                                         gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */,
-                                         false, gemmlowp_output_stage);
+    const GEMMInfo &gemm_info = GEMMInfo(false,                 // is_a_reshaped
+                                         false,                 // is_b_reshaped
+                                         true,                  // reshape_b_only_on_first_run
+                                         gemm_3d_depth,         // depth_output_gemm3d
+                                         skip_im2col,           // reinterpret_input_as_3d
+                                         false,                 // retain_internal_weights
+                                         gemmlowp_output_stage, // gemmlowp_output_stage
+                                         false,                 // fp_mixed_precision
+                                         true,                  // broadcast_bias
+                                         act_info);             // activation_info
 
     if(is_quantized)
     {
@@ -159,10 +169,8 @@
     }
     else
     {
-        // Bias does not need to be added in GEMM if im2col is being used or the Matrix Addition kernel needs to be run
-        const bool skip_bias_in_gemm = run_addition || !skip_im2col;
         // Perform validation step on Matrix multiply function
-        return CLGEMM::validate(input, weights, (skip_bias_in_gemm) ? nullptr : biases, output, 1.0f, 1.0f, gemm_info);
+        return CLGEMM::validate(input, weights, biases, output, 1.0f, 1.0f, gemm_info);
     }
 }
 
@@ -194,15 +202,14 @@
     const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform();
     const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform();
 
-    _is_prepared                = weights_info.retain_internal_weights();
-    _original_weights           = weights;
-    _is_quantized               = is_data_type_quantized_asymmetric(input->info()->data_type());
-    _data_layout                = data_layout;
-    _skip_im2col                = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
-    _skip_col2im                = data_layout == DataLayout::NHWC;
-    _append_bias                = (biases != nullptr) && (!_is_quantized);
-    _is_activationlayer_enabled = act_info.enabled();
-    _run_addition               = (_skip_im2col) && (_append_bias);
+    _is_prepared      = weights_info.retain_internal_weights();
+    _original_weights = weights;
+    _is_quantized     = is_data_type_quantized_asymmetric(input->info()->data_type());
+    _skip_im2col      = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
+    _skip_col2im      = data_layout == DataLayout::NHWC;
+
+    // Only for quantize there are few cases where we cannot fuse the activation function in GEMM
+    _fuse_activation = true;
 
     // Set the GPU target for im2col and col2im
     _im2col_kernel.set_target(CLScheduler::get().target());
@@ -211,8 +218,6 @@
     const ICLTensor *gemm_input_to_use  = input;
     ICLTensor       *gemm_output_to_use = output;
 
-    const ICLTensor *biases_to_use = (_append_bias && !_skip_im2col) ? biases : nullptr;
-
     // Get parameters from conv_info
     unsigned int stride_x = 0;
     unsigned int stride_y = 0;
@@ -230,9 +235,22 @@
 
     unsigned int mat_weights_cols = weights->info()->dimension(idx_kernels) / num_groups;
 
-    // _weights_reshaped will be auto configured in the kernel.
-    // Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM
-    _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, num_groups);
+    const ICLTensor *biases_to_use = biases;
+    bool             append_bias   = false;
+
+    if(num_groups != 1 && biases != nullptr)
+    {
+        // num_groups != 1 can only be for NCHW
+        // Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor
+        biases_to_use = nullptr;
+        append_bias   = true;
+
+        _reshape_weights.configure(weights, biases, &_weights_reshaped, num_groups);
+    }
+    else
+    {
+        _reshape_weights.configure(weights, nullptr, &_weights_reshaped, num_groups);
+    }
 
     // Create tensor to store im2col reshaped inputs
     if(!_skip_im2col)
@@ -240,7 +258,7 @@
         _memory_group.manage(&_im2col_output);
 
         // Configure and tune im2col. im2col output shape is auto-initialized
-        _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, _append_bias, dilation, num_groups);
+        _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation, num_groups);
 
         // Set quantization info
         _im2col_output.info()->set_quantization_info(input->info()->quantization_info());
@@ -249,11 +267,6 @@
         // Update GEMM input
         gemm_input_to_use = &_im2col_output;
     }
-    else if(_append_bias)
-    {
-        // Configure add bias kernel
-        _add_bias_kernel.configure(ArithmeticOperation::ADD, output, biases, output, ConvertPolicy::SATURATE);
-    }
 
     // Create GEMM output tensor
     if(!_skip_col2im)
@@ -299,16 +312,20 @@
                                                                                    ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
                                                                                  };
 
-        if(_is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0)
+        if(act_info.enabled())
         {
-            const int a_const_int = quantize_qasymm8(act_info.a(), output_quant_info);
-            const int b_const_int = quantize_qasymm8(act_info.b(), output_quant_info);
+            if(supported_acts.count(act_info.activation()) != 0)
+            {
+                const int a_const_int = quantize_qasymm8(act_info.a(), output_quant_info);
+                const int b_const_int = quantize_qasymm8(act_info.b(), output_quant_info);
 
-            min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
-            max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
-
-            // If the activation layer is RELU, BOUNDED_RELU or LU_BOUNDED_RELU, we can use the GEMMLowp output stage to perform this operation
-            _is_activationlayer_enabled = false;
+                min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
+                max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
+            }
+            else
+            {
+                _fuse_activation = false;
+            }
         }
 
         // Set the GEMMLowp output stage info
@@ -323,7 +340,7 @@
     // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
     const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0;
 
-    configure_mm(gemm_input_to_use, &_weights_reshaped, biases, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth);
+    configure_mm(gemm_input_to_use, &_weights_reshaped, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, act_info);
 
     if(!_skip_im2col)
     {
@@ -345,7 +362,7 @@
     ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h),
                              "Output shape does not match the expected one");
 
-    if(_is_activationlayer_enabled)
+    if(!_fuse_activation)
     {
         _activationlayer_function.configure(output, nullptr, act_info);
     }
@@ -382,12 +399,10 @@
     const ITensorInfo *gemm_output_to_use = output;
     const ITensorInfo *weights_to_use     = weights;
 
-    const bool is_quantized               = is_data_type_quantized_asymmetric(data_type);
-    const bool append_bias                = (biases != nullptr) && (!is_quantized);
-    const bool skip_im2col                = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
-    const bool skip_col2im                = data_layout == DataLayout::NHWC;
-    bool       is_activationlayer_enabled = act_info.enabled();
-    const bool run_addition               = (skip_im2col) && (append_bias);
+    const bool is_quantized    = is_data_type_quantized_asymmetric(data_type);
+    const bool skip_im2col     = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv_info.stride().first == 1 && conv_info.stride().second == 1);
+    const bool skip_col2im     = data_layout == DataLayout::NHWC;
+    bool       fuse_activation = true;
 
     const UniformQuantizationInfo iq_info = input->quantization_info().uniform();
     const UniformQuantizationInfo wq_info = weights->quantization_info().uniform();
@@ -429,10 +444,26 @@
 
     unsigned int mat_weights_cols = weights->dimension(idx_kernels) / num_groups;
 
-    // Output tensor auto inizialitation if not yet initialized
-    ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, is_quantized ? nullptr : biases, nullptr, num_groups));
-    weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, (append_bias && !skip_im2col), num_groups), 1, data_type);
-    weights_to_use        = &weights_reshaped_info;
+    const ITensorInfo *biases_to_use = biases;
+    bool               append_bias   = false;
+
+    if(num_groups != 1 && biases != nullptr)
+    {
+        // num_groups != 1 can only be for NCHW
+        // Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor
+        biases_to_use = nullptr;
+        append_bias   = true;
+
+        ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, biases, nullptr, num_groups));
+        weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, true, num_groups), 1, data_type);
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR(CLConvolutionLayerReshapeWeights::validate(weights, nullptr, nullptr, num_groups));
+        weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, false, num_groups), 1, data_type);
+    }
+
+    weights_to_use = &weights_reshaped_info;
 
     if(!skip_im2col)
     {
@@ -446,11 +477,6 @@
         ARM_COMPUTE_RETURN_ON_ERROR(CLIm2ColKernel::validate(input, &im2col_reshaped_info, kernel_dims, conv_info, append_bias, dilation, num_groups));
         gemm_input_to_use = &im2col_reshaped_info;
     }
-    else if(run_addition)
-    {
-        // Validate add bias kernel
-        ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, output, biases, output, ConvertPolicy::SATURATE));
-    }
 
     // Create GEMM output tensor
     if(!skip_col2im)
@@ -490,16 +516,20 @@
                                                                                    ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU
                                                                                  };
 
-        if(is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0)
+        if(act_info.enabled())
         {
-            const int a_const_int = quantize_qasymm8(act_info.a(), output_quant_info);
-            const int b_const_int = quantize_qasymm8(act_info.b(), output_quant_info);
+            if(supported_acts.count(act_info.activation()) != 0)
+            {
+                const int a_const_int = quantize_qasymm8(act_info.a(), output_quant_info);
+                const int b_const_int = quantize_qasymm8(act_info.b(), output_quant_info);
 
-            min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
-            max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
-
-            // If the activation layer is RELU, BOUNDED_RELU or LU_BOUNDED_RELU, we can use the GEMMLowp output stage to perform this operation
-            is_activationlayer_enabled = false;
+                min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int;
+                max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int;
+            }
+            else
+            {
+                fuse_activation = false;
+            }
         }
 
         // Set the GEMMLowp output stage info
@@ -513,7 +543,7 @@
     // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix
     const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0;
 
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col, run_addition));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col, act_info));
 
     // Validate Col2Im
     if(!skip_col2im)
@@ -522,7 +552,7 @@
     }
 
     //Validate Activation Layer
-    if(is_activationlayer_enabled)
+    if(!fuse_activation)
     {
         ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(output, nullptr, act_info));
     }
@@ -554,19 +584,14 @@
         _mm_gemm.run();
     }
 
-    if(_run_addition)
-    {
-        CLScheduler::get().enqueue(_add_bias_kernel);
-    }
-
     // Reshape output matrix
     if(!_skip_col2im)
     {
         CLScheduler::get().enqueue(_col2im_kernel, false);
     }
 
-    //Run Activation Layer if enabled
-    if(_is_activationlayer_enabled)
+    //Run Activation Layer if we cannot fuse in GEMM
+    if(!_fuse_activation)
     {
         _activationlayer_function.run();
     }