diff --git a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
index 02c5754..a0eb3f2 100644
--- a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
+++ b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
@@ -100,7 +100,8 @@
 {
     _type = CLKernelType::GEMM;
 }
-Status ClMatMulLowpNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info)
+Status ClMatMulLowpNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+                                          const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
@@ -111,24 +112,32 @@
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.activation() != ActivationFunction::IDENTITY && act_info.activation() != ActivationFunction::RELU
                                      && act_info.activation() != ActivationFunction::LU_BOUNDED_RELU && act_info.activation() != ActivationFunction::BOUNDED_RELU),
                                     "Activation Function specified is unsupported.");
+    const TensorShape expected_output_shape = misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info);
 
     if(dst->total_size() != 0)
     {
-        const TensorInfo tensor_info_output = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
+        const TensorInfo tensor_info_output = dst->clone()->set_tensor_shape(expected_output_shape);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_output);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
     }
 
+    if(bias != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
+        ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
+        ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != bias->dimension(0));
+    }
+
     return Status{};
 }
-void ClMatMulLowpNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+void ClMatMulLowpNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
                                          const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst, &compile_context, &matmul_kernel_info);
-    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, matmul_kernel_info);
-    ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, dst, matmul_kernel_info));
+    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst, matmul_kernel_info);
+    ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, bias, dst, matmul_kernel_info));
 
-    // output tensor auto initialization if not yet initialized
+    // dst tensor auto initialization if not yet initialized
     auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));
 
     const int  m       = dst->dimension(1);
@@ -172,7 +181,8 @@
     // Note : Offset is not negated, unlike gemmlowp kernels
     build_opts.add_option("-DLHS_OFFSET=" + support::cpp11::to_string(lqinfo.offset));
     build_opts.add_option("-DRHS_OFFSET=" + support::cpp11::to_string(rqinfo.offset));
-    build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(dqinfo.offset)); // Passed as positive (unlike the above two)
+    build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(dqinfo.offset));
+    build_opts.add_option_if(bias != nullptr, "-DBIAS");
 
     // Floating point boundaries are quantized prior to being passed as arguments.
     // Note: We expect the input and output tensors to always adopt a per-tensor quantization approach
@@ -222,17 +232,22 @@
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
 
-    const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
-    const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
-    ICLTensor       *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+    const ICLTensor *lhs  = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+    const ICLTensor *rhs  = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+    const ICLTensor *bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
+    ICLTensor       *dst  = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
-    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst);
+    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst);
 
     unsigned int idx              = 0;
     Window       window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ);
 
     add_3d_tensor_nhw_argument(idx, lhs);
     add_3d_tensor_nhw_argument(idx, rhs);
+    if(bias != nullptr)
+    {
+        add_3d_tensor_nhw_argument(idx, bias);
+    }
     add_3d_tensor_nhw_argument(idx, dst);
 
     enqueue(queue, *this, window_collapsed, lws_hint());
diff --git a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h
index 67d1a66..c908280 100644
--- a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h
+++ b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h
@@ -45,15 +45,16 @@
     /** Initialise the kernel's input and output.
      *
      * @param[in]  compile_context    The compile context to be used.
-     * @param[in]  lhs                Input tensor for the LHS matrix. Data type supported: QASYMM8_SIGNED/QASYMM8.
+     * @param[in]  lhs                Input tensor info for the LHS matrix. Data type supported: QASYMM8_SIGNED/QASYMM8.
      *                                Dimensions above 2 are collapsed onto dimension 2 and represent the batch.
-     * @param[in]  rhs                Input tensor for the RHS matrix. Data type supported: same as @p lhs.
+     * @param[in]  rhs                Input tensor info for the RHS matrix. Data type supported: same as @p lhs.
      *                                Dimensions above 2 are collapsed onto dimension 2 and represent the batch.
+     * @param[in]  bias               Bias tensor info. Can be nullptr. Data type supported: S32.
      * @param[out] dst                Output tensor info. Data type supported: same as @p lhs
      * @param[in]  matmul_kernel_info Attributes for Batch MatMul Kernel
-     * @param[in]  act_info           Class containing information about fused activation function.
+     * @param[in]  act_info           (Optional) Class containing information about fused activation function.
      */
-    void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+    void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
                    const ActivationLayerInfo &act_info = ActivationLayerInfo());
     /** Static function to check if given info will lead to a valid configuration
      *
@@ -61,7 +62,8 @@
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info = ActivationLayerInfo());
+    static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+                           const ActivationLayerInfo &act_info = ActivationLayerInfo());
 
     // Inherited methods overridden:
     void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
diff --git a/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp b/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp
index 205396a..545a5b2 100644
--- a/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp
+++ b/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp
@@ -120,7 +120,8 @@
     _type = CLKernelType::GEMM;
 }
 
-Status ClMatMulNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info)
+Status ClMatMulNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+                                      const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_UNUSED(act_info);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
@@ -130,21 +131,30 @@
     ARM_COMPUTE_RETURN_ON_ERROR(validate_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_export_to_cl_image(rhs, matmul_kernel_info));
 
+    const TensorShape expected_output_shape = misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info);
+
     if(dst->total_size() != 0)
     {
-        const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
+        const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_output_shape);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
     }
 
+    if(bias != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(bias, lhs);
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG((bias->num_dimensions() > 1), "Multi dimensional bias is unsupported.");
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(bias->dimension(0) != expected_output_shape[0], "First dimension of bias and output tensors must match.");
+    }
+
     return Status{};
 }
-void ClMatMulNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+void ClMatMulNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
                                      const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst, &compile_context, &matmul_kernel_info);
-    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, matmul_kernel_info);
-    ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, dst, matmul_kernel_info));
+    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst, matmul_kernel_info);
+    ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, bias, dst, matmul_kernel_info));
 
     // dst tensor auto initialization if not yet initialized
     auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));
@@ -176,6 +186,7 @@
     build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
     build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
     build_opts.add_option("-DK=" + support::cpp11::to_string(k));
+    build_opts.add_option_if(bias != nullptr, "-DBIAS");
     build_opts.add_option_if_else(_export_rhs_to_cl_image, "-DRHS_TENSOR_TYPE=IMAGE", "-DRHS_TENSOR_TYPE=BUFFER");
 
     // Define values for activation function
@@ -225,11 +236,12 @@
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
 
-    const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
-    const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
-    ICLTensor       *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+    const ICLTensor *lhs  = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+    const ICLTensor *rhs  = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+    const ICLTensor *bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present
+    ICLTensor       *dst  = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
-    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst);
+    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst);
 
     unsigned int idx              = 0;
     Window       window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ);
@@ -250,6 +262,10 @@
     }
 
     add_3d_tensor_nhw_argument(idx, rhs);
+    if(bias != nullptr)
+    {
+        add_3d_tensor_nhw_argument(idx, bias);
+    }
     add_3d_tensor_nhw_argument(idx, dst);
 
     enqueue(queue, *this, window_collapsed, lws_hint());
diff --git a/src/gpu/cl/kernels/ClMatMulNativeKernel.h b/src/gpu/cl/kernels/ClMatMulNativeKernel.h
index 02d8ac3..fe2b787 100644
--- a/src/gpu/cl/kernels/ClMatMulNativeKernel.h
+++ b/src/gpu/cl/kernels/ClMatMulNativeKernel.h
@@ -43,15 +43,16 @@
     /** Initialise the kernel's input and output.
      *
      * @param[in]  compile_context    The compile context to be used.
-     * @param[in]  lhs                Input tensor for the LHS matrix. Data type supported: F32/F16.
+     * @param[in]  lhs                Input tensor info for the LHS matrix. Data type supported: F32/F16.
      *                                Dimensions above 2 are collapsed onto dimension 2 and represent the batch.
-     * @param[in]  rhs                Input tensor for the RHS matrix. Data type supported: same as @p lhs.
+     * @param[in]  rhs                Input tensor info for the RHS matrix. Data type supported: same as @p lhs.
      *                                Dimensions above 2 are collapsed onto dimension 2 and represent the batch.
+     * @param[in]  bias               Bias tensor info for bias matrix. Can be nullptr. Data type supported: same as @p lhs.
      * @param[out] dst                Output tensor info. Data type supported: same as @p lhs
      * @param[in]  matmul_kernel_info Attributes for Batch MatMul Kernel
-     * @param[in]  act_info           Specifies activation function to use after Matrix multiplication. Default is Identity function.
+     * @param[in]  act_info           (Optional) Specifies activation function to use after Matrix multiplication. Default is Identity function.
      */
-    void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+    void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
                    const ActivationLayerInfo &act_info = ActivationLayerInfo());
     /** Static function to check if given info will lead to a valid configuration
      *
@@ -59,7 +60,8 @@
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info = ActivationLayerInfo());
+    static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+                           const ActivationLayerInfo &act_info = ActivationLayerInfo());
 
     // Inherited methods overridden:
     void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
diff --git a/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp
index 4630ec0..0efcfb1 100644
--- a/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp
+++ b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp
@@ -60,9 +60,9 @@
 Status validate_matmul_kernel_info(const MatMulKernelInfo &matmul_kernel_info)
 {
     const bool adj_lhs = matmul_kernel_info.adj_lhs;
-    const int m0 = matmul_kernel_info.m0;
-    const int n0 = matmul_kernel_info.n0;
-    const int k0 = matmul_kernel_info.k0;
+    const int  m0      = matmul_kernel_info.m0;
+    const int  n0      = matmul_kernel_info.n0;
+    const int  k0      = matmul_kernel_info.k0;
 
     // Validate M0
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(m0 < 1, "Only positive integers are supported for M0");
@@ -149,7 +149,7 @@
     _type = CLKernelType::GEMM;
 }
 
-Status ClMatMulNativeMMULKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info)
+Status ClMatMulNativeMMULKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16);
@@ -158,20 +158,29 @@
     ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_kernel_info(matmul_kernel_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
 
+    const TensorShape expected_output_shape = misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info);
+
     if(dst->total_size() != 0)
     {
-        const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
+        const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_output_shape);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
     }
 
+    if(bias != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG((bias->num_dimensions() > 1), "Multi dimensional bias is unsupported.");
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG(bias->dimension(0) != expected_output_shape[0], "First dimension of bias and output tensors must match.");
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, bias);
+    }
+
     return Status{};
 }
-void ClMatMulNativeMMULKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info)
+void ClMatMulNativeMMULKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
-    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, matmul_kernel_info);
-    ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, dst, matmul_kernel_info));
+    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst, matmul_kernel_info);
+    ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, bias, dst, matmul_kernel_info));
 
     // dst tensor auto initialization if not yet initialized
     auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));
@@ -207,6 +216,7 @@
     build_opts.add_option("-DMMUL_M0=" + support::cpp11::to_string(mmul_m0));
     build_opts.add_option("-DMMUL_N0=" + support::cpp11::to_string(mmul_n0));
     build_opts.add_option("-DMMUL_K0=" + support::cpp11::to_string(mmul_k0));
+    build_opts.add_option_if(bias != nullptr, "-DBIAS");
 
     std::string kernel_name("mat_mul_native_mmul");
     kernel_name += matmul_kernel_info.adj_lhs ? "_t" : "_nt";
@@ -239,15 +249,20 @@
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
 
-    const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
-    const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
-    ICLTensor       *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+    const ICLTensor *lhs  = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+    const ICLTensor *rhs  = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+    const ICLTensor *bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present
+    ICLTensor       *dst  = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
     ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
-    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst);
+    ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst);
     unsigned int idx = 0;
 
     add_3d_tensor_nhw_argument(idx, lhs);
     add_3d_tensor_nhw_argument(idx, rhs);
+    if(bias != nullptr)
+    {
+        add_3d_tensor_nhw_argument(idx, bias);
+    }
     add_3d_tensor_nhw_argument(idx, dst);
 
     // Pass m and n at runtime as signed ints, to ensure results of any subtractions they could be operand in, would still be signed.
diff --git a/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h
index 79f675d..8044897 100644
--- a/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h
+++ b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h
@@ -66,19 +66,20 @@
      * - No broadcasting in batch dimensions. I.e. batch dims must be the same across lhs, rhs and dst
      *
      * @param[in]  compile_context The compile context to be used.
-     * @param[in]  lhs             Input tensor for the LHS matrix.
-     * @param[in]  rhs             Input tensor for the RHS matrix.
+     * @param[in]  lhs             Input tensor info for the LHS matrix.
+     * @param[in]  rhs             Input tensor info for the RHS matrix.
+     * @param[in]  bias            Bias tensor info. Can be nullptr. Data type supported: Same as @p lhs.
      * @param[out] dst             Output tensor info.
      * @param[in]  matmul_info     Attributes for Batch MatMul Kernel
      */
-    void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_info);
+    void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_info);
     /** Static function to check if given info will lead to a valid configuration
      *
      * Similar to @ref ClMatMulNativeMMULKernel::configure()
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_info);
+    static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_info);
 
     // Inherited methods overridden:
     void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
diff --git a/src/gpu/cl/operators/ClFullyConnected.cpp b/src/gpu/cl/operators/ClFullyConnected.cpp
index b7ba8b8..0be3f0f 100644
--- a/src/gpu/cl/operators/ClFullyConnected.cpp
+++ b/src/gpu/cl/operators/ClFullyConnected.cpp
@@ -113,22 +113,25 @@
 
 Status validate_mm(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo *bias, const ITensorInfo &dst, const FullyConnectedLayerInfo &fc_info)
 {
-    // If weights are dynamic, data is not batched, and bias is nullptr validate using matmul.
-    const bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
-    const bool use_matmul       = !weights.are_values_constant() && !weights_reshaped && !(dst.dimension(1) > 1) && (bias == nullptr);
+    // Note : If input is dynamic and data is not batched, use matmul, else use gemm
+    const bool transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false;
+    const bool use_matmul        = !weights.are_values_constant() && !(dst.dimension(1) > 1);
+    const bool use_dynamic_gemm  = !use_matmul && !weights.are_values_constant() && transpose_weights; // use dynamic gemm as fallback for matmul
+    const bool is_quantized      = is_data_type_quantized_asymmetric(src.data_type());
 
     if(use_matmul)
     {
-        MatMulInfo m_info{};
-        m_info.adj_rhs(fc_info.transpose_weights);
+        const MatMulInfo m_info = MatMulInfo().adj_rhs(transpose_weights);
 
-        // Note: Currently, shape is [M, B0, B1]
-        // LHS is reshaped here to match ClMatMul expectations of batch index in format - [M, 1, B0, B1, .. ]
-        TensorInfo lhs_to_use{ src };
-        lhs_to_use.set_tensor_shape(get_reshaped_matmul_tensor(src.tensor_shape()));
+        // Note: LHS is reshaped here to match ClMatMul expectations of batch index - From [M, B0, B1] to [M, 1, B0, B1]
+        TensorInfo lhs_to_use = src.clone()->set_tensor_shape(get_reshaped_matmul_tensor(src.tensor_shape()));
 
-        // Operator level validation.
-        ARM_COMPUTE_RETURN_ON_ERROR(ClMatMul::validate(&lhs_to_use, &weights, &dst, m_info, fc_info.activation_info));
+        const GPUTarget                                         gpu_target  = CLScheduler::get().target();
+        std::unique_ptr<cl_matmul::IClMatMulNativeKernelConfig> t           = cl_matmul::ClMatMulNativeKernelConfigurationFactory::create(gpu_target);
+        const MatMulKernelInfo                                  kernel_info = t->configure(&lhs_to_use, &weights, m_info);
+
+        return is_quantized ? kernels::ClMatMulLowpNativeKernel::validate(&lhs_to_use, &weights, bias, &dst, kernel_info, fc_info.activation_info) :
+               kernels::ClMatMulNativeKernel::validate(&lhs_to_use, &weights, bias, &dst, kernel_info, fc_info.activation_info);
     }
     else
     {
@@ -137,7 +140,7 @@
 
         const GEMMInfo &gemm_info = GEMMInfo(false,                           // is_a_reshaped
                                              false,                           // is_b_reshaped
-                                             true,                            // reshape_b_only_on_first_run
+                                             !use_dynamic_gemm,               // reshape_b_only_on_first_run
                                              0,                               // depth_output_gemm3d
                                              false,                           // reinterpret_input_as_3d
                                              fc_info.retain_internal_weights, // retain_internal_weights
@@ -147,7 +150,7 @@
                                              true,                            // broadcast_bias
                                              ActivationLayerInfo());          // activation_info
 
-        if(is_data_type_quantized_asymmetric(src.data_type()))
+        if(is_quantized)
         {
             const UniformQuantizationInfo iq_info = src.quantization_info().uniform();
             const UniformQuantizationInfo wq_info = weights.quantization_info().uniform();
@@ -191,35 +194,33 @@
 void ClFullyConnected::configure_mm(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst,
                                     const FullyConnectedLayerInfo &fc_info)
 {
-    // If weights are dynamic, configure matmul operator - else use gemm
+    // If weights are dynamic and matmul is supported use matmul, else use gemm
     if(_use_matmul)
     {
-        // Transpose RHS as _are_weights_reshaped == false when mat_mul is used.
-        const MatMulInfo mat_info = MatMulInfo().adj_rhs(fc_info.transpose_weights);
+        // Specify whether transpose weights is necessary in matmul info
+        const MatMulInfo mat_info = MatMulInfo().adj_rhs(_transpose_weights);
 
         // Note: MatMul does not need offset negation unlike gemm
         // 1. Change shape when calling matmul to fit batch expectations.
-        _lhs_to_use = *src->clone();
-        _lhs_to_use.set_tensor_shape(get_reshaped_matmul_tensor(_lhs_to_use.tensor_shape())); // Collapse all dims > 2 into final dimension.
-        _is_quantized = is_data_type_quantized_asymmetric(_lhs_to_use.data_type());
+        _lhs_to_use = src->clone()->set_tensor_shape(get_reshaped_matmul_tensor(_lhs_to_use.tensor_shape()));
 
-        // 2. Call kernel for matmul directly.
+        // 2. Use heuristics to get kernel info object
         const GPUTarget                                         gpu_target    = CLScheduler::get().target();
         std::unique_ptr<cl_matmul::IClMatMulNativeKernelConfig> kernel_config = cl_matmul::ClMatMulNativeKernelConfigurationFactory::create(gpu_target);
+        MatMulKernelInfo                                        kernel_info   = kernel_config->configure(src, weights, mat_info);
 
-        // Configure relevant matmul kernel
-        MatMulKernelInfo kernel_info = kernel_config->configure(src, weights, mat_info);
+        // 3. Configure relevant matmul kernel
         if(_is_quantized)
         {
             _matmul_lowp_native_kernel = std::make_unique<kernels::ClMatMulLowpNativeKernel>();
             _matmul_lowp_native_kernel->set_target(gpu_target);
-            _matmul_lowp_native_kernel->configure(compile_context, src, weights, dst, kernel_info, fc_info.activation_info);
+            _matmul_lowp_native_kernel->configure(compile_context, src, weights, bias, dst, kernel_info, fc_info.activation_info);
         }
         else
         {
             _matmul_native_kernel = std::make_unique<kernels::ClMatMulNativeKernel>();
             _matmul_native_kernel->set_target(gpu_target);
-            _matmul_native_kernel->configure(compile_context, src, weights, dst, kernel_info, fc_info.activation_info);
+            _matmul_native_kernel->configure(compile_context, src, weights, bias, dst, kernel_info, fc_info.activation_info);
         }
     }
     else
@@ -230,7 +231,7 @@
 
         const GEMMInfo &gemm_info = GEMMInfo(false,                           // is_a_reshaped
                                              false,                           // is_b_reshaped
-                                             !_dynamic_weights,               // reshape_b_only_on_first_run
+                                             !_dynamic_gemm,                  // reshape_b_only_on_first_run
                                              0,                               // depth_output_gemm3d
                                              false,                           // reinterpret_input_as_3d
                                              fc_info.retain_internal_weights, // retain_internal_weights
@@ -269,7 +270,8 @@
 void ClFullyConnected::configure_conv_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst,
                                          const FullyConnectedLayerInfo &fc_info)
 {
-    ARM_COMPUTE_ERROR_ON((weights->dimension((_use_matmul) ? 0 : 1) != (src->dimension(0) * src->dimension(1) * src->dimension(2))));
+    // MatMul fuses transpose operation, so we use the first dimension for comparison where appropriate.
+    ARM_COMPUTE_ERROR_ON((weights->dimension((_use_matmul && _transpose_weights) ? 0 : 1) != (src->dimension(0) * src->dimension(1) * src->dimension(2))));
 
     // If the fully connected layer is called after a convolution layer, the input tensor must be linearized
 
@@ -288,8 +290,8 @@
 void ClFullyConnected::configure_fc_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst,
                                        const FullyConnectedLayerInfo &fc_info)
 {
-    // Compare first dimension when using matmul, as it performs transpose operation
-    ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension((_use_matmul) ? 0 : 1));
+    // MatMul fuses transpose operation, so we use the first dimension for comparison where appropriate.
+    ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension((_use_matmul && _transpose_weights) ? 0 : 1));
 
     // Configure matrix multiply kernel
     configure_mm(compile_context, src, weights, bias, dst, fc_info);
@@ -304,20 +306,18 @@
     ARM_COMPUTE_ERROR_THROW_ON(ClFullyConnected::validate(src, weights, biases, dst, fc_info));
     ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, fc_info);
 
-    _are_weights_converted = true;
-    _are_weights_reshaped  = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
-    _is_fc_after_conv      = true;
-    _is_quantized          = is_data_type_quantized_asymmetric(src->data_type());
-    _is_prepared           = fc_info.retain_internal_weights;
-    _weights_to_use        = TensorInfo(*weights);
-    _weights_to_use_idx    = ACL_SRC_1;
+    _transpose_weights  = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false;
+    _is_fc_after_conv   = true;
+    _is_quantized       = is_data_type_quantized_asymmetric(src->data_type());
+    _is_prepared        = fc_info.retain_internal_weights;
+    _weights_to_use     = TensorInfo(*weights);
+    _weights_to_use_idx = ACL_SRC_1;
 
     // When using dynamic weights - use matmul kernels.
-    // Note: We don't appear to support dynamic weights with pre-reshaped RHS.
-    // Note: No matmul with biases for the moment.
+    // Note: MatMul does not support broadcasting batch dimension, and therefore is disabled if fc is batched. Gemm is used as fallback.
     const bool is_batched_fc_layer = dst->dimension(1) > 1;
-    _dynamic_weights               = !weights->are_values_constant() && !_are_weights_reshaped;
-    _use_matmul                    = _dynamic_weights && !is_batched_fc_layer && (biases == nullptr);
+    _use_matmul                    = !weights->are_values_constant() && !is_batched_fc_layer;
+    _dynamic_gemm                  = !weights->are_values_constant() && _transpose_weights && !_use_matmul;
 
     // With the Fully Connected layer we can have 4 different cases:
     //  1) Convolution layer -> Fully Connected layer without batches
@@ -339,9 +339,8 @@
 
     ITensorInfo *weights_used = weights;
 
-    // Reshape weights if needed
-    // Not needed when matmul is in use -  MatMul has transpose RHS flags.
-    if(!_are_weights_reshaped && !_use_matmul)
+    // Reshape weights if needed - Not needed when matmul is in use as matmul fuses transpose op.
+    if(_transpose_weights && !_use_matmul)
     {
         // Reshape the weights
         _reshape_weights = std::make_unique<ClTranspose>();
@@ -361,9 +360,9 @@
                                     src->tensor_shape(),
                                     fc_info.weights_trained_layout);
 
-        weights_used           = &_converted_weights;
-        _weights_to_use_idx    = offset_int_vec(ConvertedWeights);
-        _are_weights_converted = false;
+        weights_used         = &_converted_weights;
+        _weights_to_use_idx  = offset_int_vec(ConvertedWeights);
+        _run_convert_weights = true;
     }
 
     if(_is_fc_after_conv)
@@ -398,11 +397,11 @@
             // Keep all the auxiliary tensors in case of dynamic weights as they are recalculated every time
             _aux_mem[TransposedWeights] = MemoryInfo(
                                               offset_int_vec(TransposedWeights),
-                                              _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Prepare,
+                                              _dynamic_gemm ? MemoryLifetime::Temporary : MemoryLifetime::Prepare,
                                               _reshaped_weights.total_size());
             _aux_mem[ConvertedWeights] = MemoryInfo(
                                              offset_int_vec(ConvertedWeights),
-                                             _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Prepare,
+                                             _dynamic_gemm ? MemoryLifetime::Temporary : MemoryLifetime::Prepare,
                                              _converted_weights.total_size());
         }
         else
@@ -413,11 +412,11 @@
 
             _aux_mem[TransposedWeights] = MemoryInfo(
                                               offset_int_vec(TransposedWeights),
-                                              _dynamic_weights ? MemoryLifetime::Temporary : transposed_wei_lft,
+                                              _dynamic_gemm ? MemoryLifetime::Temporary : transposed_wei_lft,
                                               _reshaped_weights.total_size());
             _aux_mem[ConvertedWeights] = MemoryInfo(
                                              offset_int_vec(ConvertedWeights),
-                                             _dynamic_weights ? MemoryLifetime::Temporary : converted_wei_lft,
+                                             _dynamic_gemm ? MemoryLifetime::Temporary : converted_wei_lft,
                                              _converted_weights.total_size());
         }
     }
@@ -434,19 +433,17 @@
     ARM_COMPUTE_RETURN_ERROR_ON(fc_info.activation_info.enabled() && is_data_type_quantized(src->data_type()) && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::RELU
                                 && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU);
 
-    const bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
-    bool       is_fc_after_conv = true;
+    const bool transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false;
+    bool       is_fc_after_conv  = true;
 
     // When using dynamic weights - use matmul kernels.
-    // Note: MatMul does not support broadcasting or biases so fallback with batched cases or when biases != nullptr.
-    // Note: Pre-Shaped RHS is a deprecated use case and is therefore not supported with matmul.
-    const bool dynamic_weights     = !weights->are_values_constant() && !weights_reshaped;
+    // Note: MatMul does not support broadcasting so fallback with batched cases.
     const bool is_batched_fc_layer = dst->dimension(1) > 1;
-    const bool use_matmul          = dynamic_weights && !is_batched_fc_layer && (biases == nullptr);
+    const bool use_matmul          = !weights->are_values_constant() && !is_batched_fc_layer;
 
     const ITensorInfo &flatten_src       = TensorInfo(src->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(src)).set_data_layout(DataLayout::NCHW));
     const ITensorInfo &reshaped_weights  = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
-    const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone());
+    const ITensorInfo &converted_weights = transpose_weights ? TensorInfo(*reshaped_weights.clone()) : TensorInfo(weights->clone()->set_is_resizable(true).reset_padding());
 
     // With the Fully Connected layer we can have 4 different cases:
     //  1) Convolution layer -> Fully Connected layer without batches
@@ -482,7 +479,8 @@
         is_fc_after_conv = src->num_dimensions() > 1;
     }
 
-    if(!weights_reshaped && !use_matmul)
+    // Transpose kernel does not run when matmul is supported as matmul fuses transpose op.
+    if(transpose_weights && !use_matmul)
     {
         // Validate reshape weights kernel
         ARM_COMPUTE_RETURN_ON_ERROR(ClTranspose::validate(weights, &reshaped_weights));
@@ -502,14 +500,9 @@
     if(is_fc_after_conv)
     {
         // Fully Connected layer after a Convolution Layer without batches
-        if(use_matmul)
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(0) != (src->dimension(0) * src->dimension(1) * src->dimension(2))));
-        }
-        else
-        {
-            ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (src->dimension(0) * src->dimension(1) * src->dimension(2))));
-        }
+        // K Index of matrix multiplication. MatMul performs transpose in kernel, so index is 0 when matmul and transpose enabled
+        const int weight_idx = (use_matmul && transpose_weights) ? 0 : 1;
+        ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(weight_idx) != (src->dimension(0) * src->dimension(1) * src->dimension(2))));
 
         // Validate flatten kernel
         ARM_COMPUTE_RETURN_ON_ERROR(ClFlatten::validate(src, &flatten_src));
@@ -518,7 +511,9 @@
     else
     {
         // Fully Connected layer after a Fully Connected Layer without batches
-        ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != weights_to_use->dimension((use_matmul) ? 0 : 1));
+        // K Index of matrix multiplication. MatMul performs transpose in kernel, so index is 0 when matmul and transpose enabled
+        const int weight_idx = (use_matmul && transpose_weights) ? 0 : 1;
+        ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != weights_to_use->dimension(weight_idx));
     }
 
     // Validate matrix multiply kernel
@@ -533,7 +528,7 @@
 
 #ifdef ARM_COMPUTE_ASSERTS_ENABLED
     ++_asrt_run_count;
-    ARM_COMPUTE_ERROR_ON(_dynamic_weights && _asrt_prepare_count != _asrt_run_count);
+    ARM_COMPUTE_ERROR_ON(_dynamic_gemm && _asrt_prepare_count != _asrt_run_count);
 #endif // ARM_COMPUTE_ASSERTS_ENABLED
 
     auto src = tensors.get_const_tensor(ACL_SRC_0);
@@ -584,11 +579,12 @@
 
 void ClFullyConnected::prepare(ITensorPack &tensors)
 {
-    if(!_is_prepared || _dynamic_weights)
+    // Note : Running prepare() each run when _use_matmul is true is unnecessary unless weights conversion is needed.
+    if(!_is_prepared || _dynamic_gemm || (_use_matmul && _run_convert_weights))
     {
 #ifdef ARM_COMPUTE_ASSERTS_ENABLED
         ++_asrt_prepare_count;
-        ARM_COMPUTE_ERROR_ON(!_dynamic_weights && _asrt_prepare_count > 1);
+        ARM_COMPUTE_ERROR_ON(!_dynamic_gemm && !_use_matmul && _asrt_prepare_count > 1);
 #endif // ARM_COMPUTE_ASSERTS_ENABLED
 
         auto weights = tensors.get_const_tensor(ACL_SRC_1);
@@ -599,8 +595,8 @@
         // Pointer to current weights
         const ITensor *cur_weights = weights;
 
-        // Reshape of the weights if needed
-        if(!_are_weights_reshaped && !_use_matmul)
+        // Reshape weights if needed. Disabled when matmul kernels are enabled as matmul fuses transpose.
+        if(_transpose_weights && !_use_matmul)
         {
             // Run reshape weights kernel and mark weights as unused
             ITensorPack transpose_pack{ { ACL_SRC, weights }, { ACL_DST, reshaped_weights.get() } };
@@ -611,7 +607,7 @@
         }
 
         // Convert weights if needed
-        if(!_are_weights_converted)
+        if(_run_convert_weights)
         {
             ITensorPack convert_pack{ { ACL_SRC, cur_weights }, { ACL_DST, converted_weights.get() } };
             _convert_weights->run(convert_pack);
@@ -623,8 +619,8 @@
         ITensorPack gemm_pack = tensors;
         gemm_pack.add_const_tensor(ACL_SRC_1, cur_weights);
 
-        // Prepare GEMM prepare and release unused weights (If not using matmul)
-        if(!_use_matmul)
+        // Prepare GEMM prepare and release unused weights
+        if(_dynamic_gemm || !_use_matmul)
         {
             if(!_is_quantized)
             {
diff --git a/src/gpu/cl/operators/ClFullyConnected.h b/src/gpu/cl/operators/ClFullyConnected.h
index 9a5ba40..5a71bd2 100644
--- a/src/gpu/cl/operators/ClFullyConnected.h
+++ b/src/gpu/cl/operators/ClFullyConnected.h
@@ -132,17 +132,18 @@
     TensorInfo _flattened_src{};
     TensorInfo _converted_weights{};
     TensorInfo _reshaped_weights{};
-    TensorInfo  _lhs_to_use{};
+    TensorInfo _lhs_to_use{};
     TensorInfo _weights_to_use{};
     int        _weights_to_use_idx{ ACL_SRC_1 };
 
-    bool _are_weights_converted{ true };
-    bool _are_weights_reshaped{ true };
+    bool _run_convert_weights{ false };
+    bool _transpose_weights{ false };
+    bool _dynamic_gemm{ false };
+    bool _use_matmul{ false };
+
     bool _is_fc_after_conv{ true };
     bool _is_quantized{ false };
     bool _is_prepared{ false };
-    bool _dynamic_weights{ false };
-    bool _use_matmul{ false };
 
 #ifdef ARM_COMPUTE_ASSERTS_ENABLED
     int _asrt_run_count {};
diff --git a/src/gpu/cl/operators/ClMatMul.cpp b/src/gpu/cl/operators/ClMatMul.cpp
index c453761..49d1412 100644
--- a/src/gpu/cl/operators/ClMatMul.cpp
+++ b/src/gpu/cl/operators/ClMatMul.cpp
@@ -61,8 +61,8 @@
 
     const bool is_quantized = is_data_type_quantized_asymmetric(lhs->data_type());
 
-    return is_quantized ? ClMatMulLowpNativeKernel::validate(lhs, rhs, dst, kernel_info, act_info) :
-           ClMatMulNativeKernel::validate(lhs, rhs, dst, kernel_info, act_info);
+    return is_quantized ? ClMatMulLowpNativeKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info) :
+           ClMatMulNativeKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
 }
 
 void ClMatMul::configure(const CLCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulInfo &matmul_info, const ActivationLayerInfo &act_info)
@@ -86,14 +86,14 @@
         _matmul_lowp_native_kernel->set_target(gpu_target);
 
         // Configure the low-precision native matrix multiply kernel
-        _matmul_lowp_native_kernel->configure(compile_context, lhs, rhs, dst, kernel_info, act_info);
+        _matmul_lowp_native_kernel->configure(compile_context, lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
     }
     else
     {
         _matmul_native_kernel->set_target(gpu_target);
 
         // Configure the native matrix multiply kernel
-        _matmul_native_kernel->configure(compile_context, lhs, rhs, dst, kernel_info, act_info);
+        _matmul_native_kernel->configure(compile_context, lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
     }
 }