diff --git a/src/runtime/CL/functions/CLQLSTMLayer.cpp b/src/runtime/CL/functions/CLQLSTMLayer.cpp
index 88c5f77..d9b5c7c 100644
--- a/src/runtime/CL/functions/CLQLSTMLayer.cpp
+++ b/src/runtime/CL/functions/CLQLSTMLayer.cpp
@@ -92,9 +92,6 @@
                              ICLTensor *cell_state_out, ICLTensor *output_state_out,
                              const LSTMParams<ICLTensor> &lstm_params)
 {
-    ARM_COMPUTE_UNUSED(forget_gate_bias);
-    ARM_COMPUTE_UNUSED(cell_bias);
-    ARM_COMPUTE_UNUSED(output_gate_bias);
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, input_to_forget_weights, input_to_cell_weights, input_to_output_weights,
                                  recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights,
                                  forget_gate_bias, cell_bias, output_gate_bias, cell_state_in, output_state_in, cell_state_out, output_state_out);
@@ -125,6 +122,21 @@
     _recurrent_to_output_weights = recurrent_to_output_weights;
     _projection_weights          = lstm_params.projection_weights();
 
+    // Layer normalization
+    _has_layer_norm = lstm_params.use_layer_norm();
+    if(_has_layer_norm)
+    {
+        set_layer_norm_weight(lstm_params.forget_layer_norm_weights(), LayerNormGate::Forget);
+        set_layer_norm_weight(lstm_params.cell_layer_norm_weights(), LayerNormGate::Cell);
+        set_layer_norm_weight(lstm_params.input_layer_norm_weights(), LayerNormGate::Input);
+        set_layer_norm_weight(lstm_params.output_layer_norm_weights(), LayerNormGate::Output);
+
+        set_layer_norm_bias(forget_gate_bias, LayerNormGate::Forget);
+        set_layer_norm_bias(cell_bias, LayerNormGate::Cell);
+        set_layer_norm_bias(lstm_params.input_gate_bias(), LayerNormGate::Input);
+        set_layer_norm_bias(output_gate_bias, LayerNormGate::Output);
+    }
+
     _has_cifg       = lstm_params.has_cifg_opt();
     _has_projection = lstm_params.has_projection();
     _has_peephole   = lstm_params.has_peephole_opt();
@@ -218,14 +230,23 @@
         _cell_to_forget_outstage_res.allocator()->allocate();
     }
 
+    CLTensor *forget_activation_input = &_recurrent_to_forget_outstage_res;
+
+    if(_has_layer_norm)
+    {
+        configure_layer_norm(LayerNormGate::Forget, &_recurrent_to_forget_outstage_res);
+        _recurrent_to_forget_outstage_res.allocator()->allocate();
+        forget_activation_input = &get_layer_norm_output(LayerNormGate::Forget);
+    }
+
     // Output quantization info of Sigmoid and Tanh activations
     const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0);
 
     const TensorInfo forget_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
     _memory_group.manage(&_forget_gate);
     _forget_gate.allocator()->init(forget_gate_info);
-    _forget_gate_sigmoid.configure(compile_context, &_recurrent_to_forget_outstage_res, &_forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
-    _recurrent_to_forget_outstage_res.allocator()->allocate();
+    _forget_gate_sigmoid.configure(compile_context, forget_activation_input, &_forget_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+    forget_activation_input->allocator()->allocate();
 
     // Modulation gate.
     const TensorInfo cell_outstage_info(mm_out_info.tensor_shape(), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.cell_intermediate_scale(), 0));
@@ -245,11 +266,20 @@
                                                      ConvertPolicy::SATURATE);
     _input_to_cell_outstage_res.allocator()->allocate();
 
+    CLTensor *cell_activation_input = &_recurrent_to_cell_outstage_res;
+
+    if(_has_layer_norm)
+    {
+        configure_layer_norm(LayerNormGate::Cell, &_recurrent_to_cell_outstage_res);
+        _recurrent_to_cell_outstage_res.allocator()->allocate();
+        cell_activation_input = &get_layer_norm_output(LayerNormGate::Cell);
+    }
+
     const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
     _memory_group.manage(&_cell_gate);
     _cell_gate.allocator()->init(cell_gate_info);
-    _cell_gate_tanh.configure(compile_context, &_recurrent_to_cell_outstage_res, &_cell_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
-    _recurrent_to_cell_outstage_res.allocator()->allocate();
+    _cell_gate_tanh.configure(compile_context, cell_activation_input, &_cell_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+    cell_activation_input->allocator()->allocate();
 
     // Input gate.
     const TensorInfo input_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
@@ -293,8 +323,17 @@
             _cell_to_input_outstage_res.allocator()->allocate();
         }
 
-        _input_gate_tanh.configure(compile_context, &_recurrent_to_input_outstage_res, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
-        _recurrent_to_input_outstage_res.allocator()->allocate();
+        CLTensor *input_activation_input = &_recurrent_to_input_outstage_res;
+
+        if(_has_layer_norm)
+        {
+            configure_layer_norm(LayerNormGate::Input, &_recurrent_to_input_outstage_res);
+            _recurrent_to_input_outstage_res.allocator()->allocate();
+            input_activation_input = &get_layer_norm_output(LayerNormGate::Input);
+        }
+
+        _input_gate_tanh.configure(compile_context, input_activation_input, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
+        input_activation_input->allocator()->allocate();
     }
     // Cell.
     // TODO(COMPMID-3396): Perform multiplication in the quantized domain in CLPixelWiseMultiplicationKernel
@@ -344,11 +383,20 @@
         _mul_cell_to_output_res.allocator()->allocate();
     }
 
+    CLTensor *output_activation_input = &_recurrent_to_output_outstage_res;
+
+    if(_has_layer_norm)
+    {
+        configure_layer_norm(LayerNormGate::Output, &_recurrent_to_output_outstage_res);
+        _recurrent_to_output_outstage_res.allocator()->allocate();
+        output_activation_input = &get_layer_norm_output(LayerNormGate::Output);
+    }
+
     const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
     _memory_group.manage(&_output_gate);
     _output_gate.allocator()->init(output_gate_info);
-    _output_gate_sigmoid.configure(compile_context, &_recurrent_to_output_outstage_res, &_output_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
-    _recurrent_to_output_outstage_res.allocator()->allocate();
+    _output_gate_sigmoid.configure(compile_context, output_activation_input, &_output_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC));
+    output_activation_input->allocator()->allocate();
 
     // Hidden.
     _hidden_tanh.configure(compile_context, cell_state_out, &_input_gate, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f));
@@ -525,6 +573,8 @@
     gemmlowp_info.gemmlowp_max_bound = std::numeric_limits<int16_t>::max();
     gemmlowp_info.output_data_type   = DataType::QSYMM16;
 
+    const bool has_layer_norm = lstm_params.use_layer_norm();
+
     // Forget gate.
     const TensorInfo forget_outstage_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, QuantizationInfo(lstm_params.forget_intermediate_scale(), 0));
     const TensorInfo mm_out_info(TensorShape(num_units, batch_size), 1, DataType::S32);
@@ -547,6 +597,13 @@
         ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &forget_outstage_info, &forget_outstage_info, &forget_outstage_info, ConvertPolicy::SATURATE));
     }
 
+    if(has_layer_norm)
+    {
+        const ITensorInfo *w_info = lstm_params.forget_layer_norm_weights();
+        const ITensorInfo *b_info = forget_gate_bias;
+        ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(forget_outstage_info, *w_info, *b_info));
+    }
+
     // Output quantization info of Sigmoid and Tanh activations
     const QuantizationInfo sigmoid_tanh_outqinfo(1.f / 32768.f, 0);
 
@@ -563,6 +620,13 @@
 
     ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &cell_outstage_info, &cell_outstage_info, &cell_outstage_info, ConvertPolicy::SATURATE));
 
+    if(has_layer_norm)
+    {
+        const ITensorInfo *w_info = lstm_params.cell_layer_norm_weights();
+        const ITensorInfo *b_info = cell_bias;
+        ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(cell_outstage_info, *w_info, *b_info));
+    }
+
     const TensorInfo cell_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
     ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&cell_outstage_info, &cell_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
 
@@ -602,6 +666,13 @@
             ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &input_outstage_info, &input_outstage_info, &input_outstage_info, ConvertPolicy::SATURATE));
         }
 
+        if(has_layer_norm)
+        {
+            const ITensorInfo *w_info = lstm_params.input_layer_norm_weights();
+            const ITensorInfo *b_info = lstm_params.input_gate_bias();
+            ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(cell_outstage_info, *w_info, *b_info));
+        }
+
         ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&input_outstage_info, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH, 1.f, 1.f)));
     }
     // Cell.
@@ -634,6 +705,13 @@
         ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &output_outstage_info, &output_outstage_info, &output_outstage_info, ConvertPolicy::SATURATE));
     }
 
+    if(has_layer_norm)
+    {
+        const ITensorInfo *w_info = lstm_params.output_layer_norm_weights();
+        const ITensorInfo *b_info = output_gate_bias;
+        ARM_COMPUTE_RETURN_ON_ERROR(validate_layer_norm(output_outstage_info, *w_info, *b_info));
+    }
+
     const TensorInfo output_gate_info(TensorShape(num_units, batch_size), 1, DataType::QSYMM16, sigmoid_tanh_outqinfo);
     ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&output_outstage_info, &output_gate_info, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC)));
 
@@ -715,6 +793,11 @@
         CLScheduler::get().enqueue(_accumulate_cell_forget);
     }
 
+    if(_has_layer_norm)
+    {
+        CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Forget));
+    }
+
     _forget_gate_sigmoid.run();
 
     // Modulation gate.
@@ -725,6 +808,11 @@
     _recurrent_to_cell_outstage.run();
     CLScheduler::get().enqueue(_accumulate_input_recurrent_modulation);
 
+    if(_has_layer_norm)
+    {
+        CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Cell));
+    }
+
     _cell_gate_tanh.run();
 
     // Input gate
@@ -747,6 +835,11 @@
             CLScheduler::get().enqueue(_accumulate_cell_input);
         }
 
+        if(_has_layer_norm)
+        {
+            CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Input));
+        }
+
         _input_gate_tanh.run();
     }
 
@@ -771,6 +864,11 @@
         CLScheduler::get().enqueue(_accumulate_cell_to_output);
     }
 
+    if(_has_layer_norm)
+    {
+        CLScheduler::get().enqueue(get_layer_norm(LayerNormGate::Output));
+    }
+
     _output_gate_sigmoid.run();
 
     // Hidden.