diff --git a/1.2/HalPolicy.cpp b/1.2/HalPolicy.cpp
index 9e547fa..d55e587 100644
--- a/1.2/HalPolicy.cpp
+++ b/1.2/HalPolicy.cpp
@@ -100,7 +100,7 @@
         case V1_2::OperationType::QUANTIZE:
             return ConvertQuantize(operation, model, data);
         case V1_2::OperationType::QUANTIZED_16BIT_LSTM:
-            return ConvertQuantizedLstm(operation, model, data);
+            return ConvertQuantized16BitLstm(operation, model, data);
         case V1_2::OperationType::RELU:
             return ConvertReLu(operation, model, data);
         case V1_2::OperationType::RELU1:
@@ -338,10 +338,10 @@
     return ::ConvertQuantize<hal_1_2::HalPolicy>(operation, model, data);
 }
 
-bool HalPolicy::ConvertQuantizedLstm(const Operation& operation, const Model& model, ConversionData& data)
+bool HalPolicy::ConvertQuantized16BitLstm(const Operation& operation, const Model& model, ConversionData& data)
 {
-    ALOGV("hal_1_2::HalPolicy::ConvertQuantizedLstm()");
-    return ::ConvertQuantizedLstm<hal_1_2::HalPolicy>(operation, model, data);
+    ALOGV("hal_1_2::HalPolicy::ConvertQuantized16BitLstm()");
+    return ::ConvertQuantized16BitLstm<hal_1_2::HalPolicy>(operation, model, data);
 }
 
 bool HalPolicy::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data)
diff --git a/1.2/HalPolicy.hpp b/1.2/HalPolicy.hpp
index b127a63..ce43a6e 100644
--- a/1.2/HalPolicy.hpp
+++ b/1.2/HalPolicy.hpp
@@ -110,7 +110,7 @@
 
     static bool ConvertQuantize(const Operation& operation, const Model& model, ConversionData& data);
 
-    static bool ConvertQuantizedLstm(const Operation& operation, const Model& model, ConversionData& data);
+    static bool ConvertQuantized16BitLstm(const Operation& operation, const Model& model, ConversionData& data);
 
     static bool ConvertReLu(const Operation& operation, const Model& model, ConversionData& data);
 
diff --git a/1.3/HalPolicy.cpp b/1.3/HalPolicy.cpp
index b2b8a86..1077b78 100644
--- a/1.3/HalPolicy.cpp
+++ b/1.3/HalPolicy.cpp
@@ -103,8 +103,10 @@
             return ConvertPrelu(operation, model, data);
         case V1_3::OperationType::QUANTIZE:
             return ConvertQuantize(operation, model, data);
-        case V1_3::OperationType::QUANTIZED_16BIT_LSTM:
+        case V1_3::OperationType::QUANTIZED_LSTM:
             return ConvertQuantizedLstm(operation, model, data);
+        case V1_3::OperationType::QUANTIZED_16BIT_LSTM:
+            return ConvertQuantized16BitLstm(operation, model, data);
         case V1_3::OperationType::RELU:
             return ConvertReLu(operation, model, data);
         case V1_3::OperationType::RELU1:
@@ -370,6 +372,12 @@
     return ::ConvertQuantizedLstm<hal_1_3::HalPolicy>(operation, model, data);
 }
 
+bool HalPolicy::ConvertQuantized16BitLstm(const Operation& operation, const Model& model, ConversionData& data)
+{
+    ALOGV("hal_1_3::HalPolicy::ConvertQuantized16BitLstm()");
+    return ::ConvertQuantized16BitLstm<hal_1_3::HalPolicy>(operation, model, data);
+}
+
 bool HalPolicy::ConvertReLu(const Operation& operation, const Model& model, ConversionData& data)
 {
     ALOGV("hal_1_3::HalPolicy::ConvertReLu()");
diff --git a/1.3/HalPolicy.hpp b/1.3/HalPolicy.hpp
index c601942..b59710a 100644
--- a/1.3/HalPolicy.hpp
+++ b/1.3/HalPolicy.hpp
@@ -117,6 +117,8 @@
 
     static bool ConvertQuantizedLstm(const Operation& operation, const Model& model, ConversionData& data);
 
+    static bool ConvertQuantized16BitLstm(const Operation& operation, const Model& model, ConversionData& data);
+
     static bool ConvertReLu(const Operation& operation, const Model& model, ConversionData& data);
 
     static bool ConvertReLu1(const Operation& operation, const Model& model, ConversionData& data);
diff --git a/ConversionUtils.hpp b/ConversionUtils.hpp
index 8313d04..5a11131 100644
--- a/ConversionUtils.hpp
+++ b/ConversionUtils.hpp
@@ -877,35 +877,40 @@
                     HalOperandType type,
                     OutputType& outValue,
                     const HalModel& model,
-                    const ConversionData& data)
+                    const ConversionData& data,
+                    bool optional = false)
 {
     using HalOperand = typename HalPolicy::Operand;
 
     const HalOperand* operand = GetInputOperand<HalPolicy>(operation, inputIndex, model);
-    if (!operand)
+    if (!optional && !operand)
     {
         return Fail("%s: invalid input operand at index %i", __func__, inputIndex);
     }
 
-    if (operand->type != type)
+    if (!optional && operand->type != type)
     {
         return Fail("%s: unexpected operand type: %s (should be %s)",
                     __func__, toString(operand->type).c_str(), toString(type).c_str());
     }
 
-    if (operand->location.length != sizeof(OutputType))
+    if (!optional && operand->location.length != sizeof(OutputType))
     {
         return Fail("%s: incorrect operand location length: %i (should be %i)",
                     __func__, operand->location.length, sizeof(OutputType));
     }
 
     const void* valueAddress = GetOperandValueReadOnlyAddress<HalPolicy>(*operand, model, data);
-    if (!valueAddress)
+    if (!optional && !valueAddress)
     {
         return Fail("%s: failed to get address for operand", __func__);
     }
 
-    outValue = *(static_cast<const OutputType*>(valueAddress));
+    if(!optional)
+    {
+        outValue = *(static_cast<const OutputType*>(valueAddress));
+    }
+
     return true;
 }
 
@@ -1374,7 +1379,8 @@
                                   armnn::IConnectableLayer& layer,
                                   uint32_t layerOutputIndex,
                                   const HalModel& model,
-                                  ConversionData& data)
+                                  ConversionData& data,
+                                  const armnn::TensorInfo* overrideOutputInfo = nullptr)
 {
     using HalOperand = typename HalPolicy::Operand;
 
@@ -1389,7 +1395,14 @@
     const uint32_t operandIndex = operation.outputs[operationOutputIndex];
     data.m_OutputSlotForOperand[operandIndex] = &outputSlot;
 
-    outputSlot.SetTensorInfo(GetTensorInfoForOperand(*outputOperand));
+    if (overrideOutputInfo == nullptr)
+    {
+        outputSlot.SetTensorInfo(GetTensorInfoForOperand(*outputOperand));
+    }
+    else
+    {
+        outputSlot.SetTensorInfo(*overrideOutputInfo);
+    }
 
     return true;
 }
diff --git a/ConversionUtils_1_2.hpp b/ConversionUtils_1_2.hpp
index c87cb03..29367f2 100644
--- a/ConversionUtils_1_2.hpp
+++ b/ConversionUtils_1_2.hpp
@@ -1497,11 +1497,11 @@
 template<typename HalPolicy,
          typename HalOperation = typename HalPolicy::Operation,
          typename HalModel     = typename HalPolicy::Model>
-bool ConvertQuantizedLstm(const HalOperation& operation, const HalModel& model, ConversionData& data)
+bool ConvertQuantized16BitLstm(const HalOperation& operation, const HalModel& model, ConversionData& data)
 {
     using HalOperand = typename HalPolicy::Operand;
 
-    ALOGV("HalPolicy::ConvertQuantizedLstm()");
+    ALOGV("HalPolicy::ConvertQuantized16BitLstm()");
 
     //Inputs:
     // 0: The input: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBatches, inputSize]
diff --git a/ConversionUtils_1_3.hpp b/ConversionUtils_1_3.hpp
index 5014e75..4714b84 100644
--- a/ConversionUtils_1_3.hpp
+++ b/ConversionUtils_1_3.hpp
@@ -65,4 +65,495 @@
     return ::ConvertToActivation<HalPolicy>(operation, __func__, desc, model, data);
 }
 
+template<typename HalPolicy,
+         typename HalOperation = typename HalPolicy::Operation,
+         typename HalModel     = typename HalPolicy::Model>
+bool ConvertQuantizedLstm(const HalOperation& operation, const HalModel& model, ConversionData& data)
+{
+    using HalOperand     = typename HalPolicy::Operand;
+    using HalOperandType = typename HalPolicy::OperandType;
+
+    ALOGV("HalPolicy::ConvertQuantizedLstm()");
+
+    //Inputs:
+    // 0: The input: A 2-D tensor of type ANEURALNETWORKS_TENSOR_QUANT8_ASYMM and shape [numBatches, inputSize]
+    //    specifying the input to the LSTM cell. Tensor is quantized with a fixed quantization range of -1, 127/128.
+    LayerInputHandle input = ConvertToLayerInputHandle<HalPolicy>(operation, 0, model, data);
+    if (!input.IsValid())
+    {
+        return Fail("%s: Could not read input 0: input", __func__);
+    }
+
+    // 18: The output state: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM, of shape [batch_size, output_size].
+    LayerInputHandle outputStatePrevTimeStep = ConvertToLayerInputHandle<HalPolicy>(operation, 18, model, data);
+    if (!outputStatePrevTimeStep.IsValid())
+    {
+        return Fail("%s: Could not read input 18: outputStatePrevTimeStep", __func__);
+    }
+
+    // 19: The cell state: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, of shape [batch_size, num_units].
+    LayerInputHandle cellStatePrevTimeStep = ConvertToLayerInputHandle<HalPolicy>(operation, 19, model, data);
+    if (!cellStatePrevTimeStep.IsValid())
+    {
+        return Fail("%s: Could not read input 19: cellStatePrevTimeStep", __func__);
+    }
+
+    // Get the mandatory input tensors:
+
+    // 02: The input-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape
+    //     [num_units, input_size].
+    const ConstTensorPin inputToForgetWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 2, model, data);
+
+    // 03: The input-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape
+    // [num_units, input_size].
+    const ConstTensorPin inputToCellWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 3, model, data);
+
+    // 04: The input-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape
+    //     [num_units, input_size].
+    const ConstTensorPin inputToOutputWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 4, model, data);
+
+    // 06: The recurrent-to-forget weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape
+    //     [num_units, output_size].
+    const ConstTensorPin recurrentToForgetWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 6, model, data);
+
+    // 07: The recurrent-to-cell weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape
+    //     [num_units, output_size].
+    const ConstTensorPin recurrentToCellWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 7, model, data);
+
+    // 08: The recurrent-to-output weights: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape
+    //     [num_units, output_size].
+    const ConstTensorPin recurrentToOutputWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 8, model, data);
+
+    // 13: The forget gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, of shape [num_units].
+    const ConstTensorPin forgetGateBiasPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 13, model, data);
+
+    // 14: The cell bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, of shape [num_units].
+    const ConstTensorPin cellBiasPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 14, model, data);
+
+    // 15: The output gate bias: A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, of shape [num_units].
+    const ConstTensorPin outputGateBiasPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation, 15, model, data);
+
+    if (!inputToForgetWeightsPin.IsValid() ||
+        !inputToCellWeightsPin.IsValid() ||
+        !inputToOutputWeightsPin.IsValid() ||
+        !recurrentToForgetWeightsPin.IsValid() ||
+        !recurrentToCellWeightsPin.IsValid() ||
+        !recurrentToOutputWeightsPin.IsValid() ||
+        !forgetGateBiasPin.IsValid() ||
+        !cellBiasPin.IsValid() ||
+        !outputGateBiasPin.IsValid())
+    {
+        return Fail("%s: Operation has invalid tensor inputs", __func__);
+    }
+
+    // Get the optional input tensors:
+
+    // 01: The input-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape
+    //     [num_units, input_size], where “num_units” corresponds to the number of cell units.
+    const ConstTensorPin inputToInputWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         1,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    // 05: The recurrent-to-input weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape
+    //     [num_units, output_size], where “output_size” corresponds to either the number of cell units (i.e.,
+    //     “num_units”), or the second dimension of the “projection_weights”, if defined.
+    const ConstTensorPin recurrentToInputWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         5,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    // 09: The cell-to-input weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, of shape
+    // [num_units].
+    const ConstTensorPin cellToInputWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         9,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    // 10: The cell-to-forget weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, of shape
+    // [num_units].
+    const ConstTensorPin cellToForgetWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         10,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    // 11: The cell-to-output weights: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, of shape
+    // [num_units].
+    const ConstTensorPin cellToOutputWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         11,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    // 12: The input gate bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, of shape [num_units].
+    const ConstTensorPin inputGateBiasPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         12,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    // 16: The projection weights: Optional. A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_SYMM, of shape
+    //     [output_size, num_units].
+    const ConstTensorPin projectionWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         16,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    // 17: The projection bias: Optional. A 1-D tensor of ANEURALNETWORKS_TENSOR_INT32, of shape [output_size].
+    const ConstTensorPin projectionBiasPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         17,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    if ((!inputToInputWeightsPin.IsValid() && !inputToInputWeightsPin.IsOptional())
+        || (!recurrentToInputWeightsPin.IsValid() && !recurrentToInputWeightsPin.IsOptional())
+        || (!cellToInputWeightsPin.IsValid() && !cellToInputWeightsPin.IsOptional())
+        || (!cellToForgetWeightsPin.IsValid() && !cellToForgetWeightsPin.IsOptional())
+        || (!cellToOutputWeightsPin.IsValid() && !cellToOutputWeightsPin.IsOptional())
+        || (!inputGateBiasPin.IsValid() && !inputGateBiasPin.IsOptional())
+        || (!projectionWeightsPin.IsValid() && !projectionWeightsPin.IsOptional())
+        || (!projectionBiasPin.IsValid() && !projectionBiasPin.IsOptional()))
+    {
+        return Fail("%s: Operation has invalid tensor inputs", __func__);
+    }
+
+
+    // Get the optional normalization tensors
+
+    // 20: The input layer normalization weights. A 1-D tensor of shape [num_units] ANEURALNETWORKS_TENSOR_QUANT16_SYMM.
+    //     Used to rescale normalized inputs to activation at input gate.
+    const ConstTensorPin inputLayerNormWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         20,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    // 21: The forget layer normalization weights. A 1-D tensor of shape [num_units] ANEURALNETWORKS_TENSOR_QUANT16_SYMM
+    //     Used to rescale normalized inputs to activation at forget gate.
+    const ConstTensorPin forgetLayerNormWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         21,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    // 22: The cell layer normalization weights. A 1-D tensor of shape [num_units] ANEURALNETWORKS_TENSOR_QUANT16_SYMM.
+    //     Used to rescale normalized inputs to activation at cell gate.
+    const ConstTensorPin cellLayerNormWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         22,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    // 23: The output layer normalization weights. A 1-D tensor of shape [num_units].
+    //     Used to rescale normalized inputs to activation at output gate.
+    const ConstTensorPin outputLayerNormWeightsPin =
+        ConvertOperationInputToConstTensorPin<HalPolicy>(operation,
+                                                         23,
+                                                         model,
+                                                         data,
+                                                         g_DontPermute,
+                                                         nullptr,
+                                                         true);
+
+    if ((!inputLayerNormWeightsPin.IsValid() && !inputLayerNormWeightsPin.IsOptional())
+        || (!forgetLayerNormWeightsPin.IsValid() && !forgetLayerNormWeightsPin.IsOptional())
+        || (!cellLayerNormWeightsPin.IsValid() && !cellLayerNormWeightsPin.IsOptional())
+        || (!outputLayerNormWeightsPin.IsValid() && !outputLayerNormWeightsPin.IsOptional()))
+    {
+        return Fail("%s: Operation has invalid tensor inputs", __func__);
+    }
+
+    // Get the optional input scalars:
+    // 24: The cell clip:  If provided the cell state is clipped by this value prior to the cell output activation.
+    // 25: The projection clip: If provided and projection is enabled, this is used for clipping the projected values.
+
+    // Get the mandatory input scalars:
+    // 26: The scale of the intermediate result of matmul, i.e. input to layer normalization, at input gate.
+    // 27: The scale of the intermediate result of matmul, i.e. input to layer normalization, at forget gate.
+    // 28: The scale of the intermediate result of matmul, i.e. input to layer normalization, at cell gate.
+    // 29: The scale of the intermediate result of matmul, i.e. input to layer normalization, at output gate.
+    // 30: The zero point of the hidden state, i.e. input to projection.
+    // 31: The scale of the hidden state, i.e. input to projection.
+    float cellClip, projClip, matMulInputGate, matMulForgetGate, matMulCellGate, matMulOutputGate, projInputScale;
+    int projInputZeroPoint;
+
+    if (!GetInputScalar<HalPolicy>(operation, 24, HalOperandType::FLOAT32, cellClip, model, data, true) ||
+        !GetInputScalar<HalPolicy>(operation, 25, HalOperandType::FLOAT32, projClip, model, data, true) ||
+        !GetInputScalar<HalPolicy>(operation, 26, HalOperandType::FLOAT32, matMulInputGate, model, data) ||
+        !GetInputScalar<HalPolicy>(operation, 27, HalOperandType::FLOAT32, matMulForgetGate, model, data) ||
+        !GetInputScalar<HalPolicy>(operation, 28, HalOperandType::FLOAT32, matMulCellGate, model, data) ||
+        !GetInputScalar<HalPolicy>(operation, 29, HalOperandType::FLOAT32, matMulOutputGate, model, data) ||
+        !GetInputScalar<HalPolicy>(operation, 30, HalOperandType::FLOAT32, projInputScale, model, data) ||
+        !GetInputScalar<HalPolicy>(operation, 31, HalOperandType::FLOAT32, projInputZeroPoint, model, data))
+    {
+        return Fail("%s: Operation has invalid scalar inputs", __func__);
+    }
+
+    // Outputs:
+    // 0: The output state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, of shape [batch_size,
+    // output_size].
+    const HalOperand* outputStateOut = GetOutputOperand<HalPolicy>(operation, 0, model);
+    if (!outputStateOut)
+    {
+        return Fail("%s: Could not read output 0: outputStateOut", __func__);
+    }
+
+    // 1: The cell state (out): A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT16_SYMM, of shape [batch_size, num_units].
+    const HalOperand* cellStateOut = GetOutputOperand<HalPolicy>(operation, 1, model);
+    if (!cellStateOut)
+    {
+        return Fail("%s: Could not read output 1: cellStateOut", __func__);
+    }
+
+    // 2: The output: A 2-D tensor of ANEURALNETWORKS_TENSOR_QUANT8_ASYMM_SIGNED, of shape [batch_size, output_size].
+    // This is effectively the same as the current “output state (out)” value.
+    const HalOperand* output = GetOutputOperand<HalPolicy>(operation, 2, model);
+    if (!output)
+    {
+        return Fail("%s: Could not read output 2: output", __func__);
+    }
+
+    // set the params structure for the AddLstmLayer call
+    LstmInputParams params;
+    params.m_InputToInputWeights = inputToInputWeightsPin.GetConstTensorPtr();
+    params.m_InputToForgetWeights = inputToForgetWeightsPin.GetConstTensorPtr();
+    params.m_InputToCellWeights = inputToCellWeightsPin.GetConstTensorPtr();
+    params.m_InputToOutputWeights = inputToOutputWeightsPin.GetConstTensorPtr();
+    params.m_RecurrentToInputWeights = recurrentToInputWeightsPin.GetConstTensorPtr();
+    params.m_RecurrentToForgetWeights = recurrentToForgetWeightsPin.GetConstTensorPtr();
+    params.m_RecurrentToCellWeights = recurrentToCellWeightsPin.GetConstTensorPtr();
+    params.m_RecurrentToOutputWeights = recurrentToOutputWeightsPin.GetConstTensorPtr();
+    params.m_CellToInputWeights = cellToInputWeightsPin.GetConstTensorPtr();
+    params.m_CellToForgetWeights = cellToForgetWeightsPin.GetConstTensorPtr();
+    params.m_CellToOutputWeights = cellToOutputWeightsPin.GetConstTensorPtr();
+    params.m_InputGateBias = inputGateBiasPin.GetConstTensorPtr();
+    params.m_ForgetGateBias = forgetGateBiasPin.GetConstTensorPtr();
+    params.m_CellBias = cellBiasPin.GetConstTensorPtr();
+    params.m_OutputGateBias = outputGateBiasPin.GetConstTensorPtr();
+    params.m_ProjectionWeights = projectionWeightsPin.GetConstTensorPtr();
+    params.m_ProjectionBias = projectionBiasPin.GetConstTensorPtr();
+    params.m_InputLayerNormWeights = inputLayerNormWeightsPin.GetConstTensorPtr();
+    params.m_ForgetLayerNormWeights = forgetLayerNormWeightsPin.GetConstTensorPtr();
+    params.m_CellLayerNormWeights = cellLayerNormWeightsPin.GetConstTensorPtr();
+    params.m_OutputLayerNormWeights = outputLayerNormWeightsPin.GetConstTensorPtr();
+
+    // set the layer descriptor
+    QLstmDescriptor desc;
+    desc.m_CellClip = cellClip;
+    desc.m_ProjectionClip = projClip;
+    desc.m_CifgEnabled = (params.m_InputToInputWeights == nullptr ||
+                          params.m_RecurrentToInputWeights == nullptr ||
+                          params.m_InputGateBias == nullptr);
+    desc.m_PeepholeEnabled = (params.m_CellToForgetWeights != nullptr ||
+                              params.m_CellToOutputWeights != nullptr);
+    desc.m_ProjectionEnabled = (params.m_ProjectionWeights != nullptr);
+    desc.m_LayerNormEnabled = (params.m_InputLayerNormWeights != nullptr ||
+                               params.m_ForgetLayerNormWeights != nullptr ||
+                               params.m_CellLayerNormWeights != nullptr ||
+                               params.m_OutputLayerNormWeights != nullptr);
+    desc.m_InputIntermediateScale = matMulInputGate;
+    desc.m_ForgetIntermediateScale = matMulForgetGate;
+    desc.m_CellIntermediateScale = matMulCellGate;
+    desc.m_OutputIntermediateScale = matMulOutputGate;
+    desc.m_HiddenStateScale = projInputScale;
+    desc.m_HiddenStateZeroPoint = projInputZeroPoint;
+
+    // validate the optional input groups
+    if (desc.m_CifgEnabled &&
+        (params.m_InputToInputWeights != nullptr ||
+         params.m_RecurrentToInputWeights != nullptr ||
+         params.m_InputGateBias != nullptr))
+    {
+        return Fail("%s: All, or none, of input-to-input weights, recurrent-to-input weights,"
+                    " and input gate bias must be provided", __func__);
+    }
+
+    if (!desc.m_ProjectionEnabled && params.m_ProjectionBias != nullptr)
+    {
+        return Fail("%s: projection bias should not be provided without projection weights", __func__);
+    }
+
+    if (desc.m_PeepholeEnabled &&
+        (params.m_CellToForgetWeights == nullptr ||
+         params.m_CellToOutputWeights == nullptr ||
+         (!desc.m_CifgEnabled && params.m_CellToInputWeights == nullptr)))
+    {
+        return Fail("%s: All, or none, of cell-to-forget weights and cell-to-output weights must be provided"
+                    " and, if CIFG is not enabled, cell-to-input weights must also be provided", __func__);
+    }
+
+    if (desc.m_LayerNormEnabled &&
+        (params.m_ForgetLayerNormWeights == nullptr ||
+         params.m_CellLayerNormWeights == nullptr ||
+         params.m_OutputLayerNormWeights == nullptr ||
+         (!desc.m_CifgEnabled && params.m_InputLayerNormWeights == nullptr)))
+    {
+        return Fail("%s: All, or none, of forget-norm weights, cell-norm weights and output-norm weights must be"
+                    " provided and, if CIFG is not enabled, input-norm weights must also be provided", __func__);
+    }
+
+
+    // Basic parameters
+    LstmInputParamsInfo paramsInfo;
+    paramsInfo.m_InputToForgetWeights     = &(params.m_InputToForgetWeights->GetInfo());
+    paramsInfo.m_InputToCellWeights       = &(params.m_InputToCellWeights->GetInfo());
+    paramsInfo.m_InputToOutputWeights     = &(params.m_InputToOutputWeights->GetInfo());
+    paramsInfo.m_RecurrentToForgetWeights = &(params.m_RecurrentToForgetWeights->GetInfo());
+    paramsInfo.m_RecurrentToCellWeights   = &(params.m_RecurrentToCellWeights->GetInfo());
+    paramsInfo.m_RecurrentToOutputWeights = &(params.m_RecurrentToOutputWeights->GetInfo());
+    paramsInfo.m_ForgetGateBias           = &(params.m_ForgetGateBias->GetInfo());
+    paramsInfo.m_CellBias                 = &(params.m_CellBias->GetInfo());
+    paramsInfo.m_OutputGateBias           = &(params.m_OutputGateBias->GetInfo());
+
+    // Inputs
+    const TensorInfo& inputInfo = input.GetTensorInfo();
+    const TensorInfo& outputStatePrevTimeStepInfo = outputStatePrevTimeStep.GetTensorInfo();
+    const TensorInfo& cellStatePrevTimeStepInfo = cellStatePrevTimeStep.GetTensorInfo();
+
+    // Outputs
+    TensorInfo outputStateOutInfo = GetTensorInfoForOperand(*outputStateOut);
+    TensorInfo outputInfo = GetTensorInfoForOperand(*output);
+    const TensorInfo& cellStateOutInfo = GetTensorInfoForOperand(*cellStateOut);
+
+    // Optional parameters
+    if (!desc.m_CifgEnabled)
+    {
+        paramsInfo.m_InputToInputWeights = &(params.m_InputToInputWeights->GetInfo());
+        paramsInfo.m_RecurrentToInputWeights = &(params.m_RecurrentToInputWeights->GetInfo());
+        if (desc.m_PeepholeEnabled)
+        {
+            paramsInfo.m_CellToInputWeights = &(params.m_CellToInputWeights->GetInfo());
+        }
+        paramsInfo.m_InputGateBias = &(params.m_InputGateBias->GetInfo());
+    }
+
+
+    if (desc.m_ProjectionEnabled)
+    {
+        paramsInfo.m_ProjectionWeights = &(params.m_ProjectionWeights->GetInfo());
+        if (params.m_ProjectionBias != nullptr)
+        {
+            paramsInfo.m_ProjectionBias = &(params.m_ProjectionBias->GetInfo());
+        }
+    }
+    else
+    {
+        // If Projection is disabled, override non-const outputs to change the quant info with hidden params, then
+        // create a new const TensorInfo based on this
+        outputStateOutInfo.SetQuantizationScale(projInputScale);
+        outputStateOutInfo.SetQuantizationOffset(projInputZeroPoint);
+        outputInfo.SetQuantizationScale(projInputScale);
+        outputInfo.SetQuantizationOffset(projInputZeroPoint);
+    }
+
+    const TensorInfo constOutputStateOutInfo(outputStateOutInfo);
+    const TensorInfo constOutputInfo(outputInfo);
+
+    if (desc.m_PeepholeEnabled)
+    {
+        paramsInfo.m_CellToForgetWeights = &(params.m_CellToForgetWeights->GetInfo());
+        paramsInfo.m_CellToOutputWeights = &(params.m_CellToOutputWeights->GetInfo());
+    }
+
+    if (desc.m_LayerNormEnabled)
+    {
+        if(!desc.m_CifgEnabled)
+        {
+            paramsInfo.m_InputLayerNormWeights = &(params.m_InputLayerNormWeights->GetInfo());
+        }
+        paramsInfo.m_ForgetLayerNormWeights = &(params.m_ForgetLayerNormWeights->GetInfo());
+        paramsInfo.m_CellLayerNormWeights = &(params.m_CellLayerNormWeights->GetInfo());
+        paramsInfo.m_OutputLayerNormWeights = &(params.m_OutputLayerNormWeights->GetInfo());
+    }
+
+    // Check if the layer is supported
+
+    if (IsDynamicTensor(constOutputStateOutInfo) ||
+        IsDynamicTensor(cellStateOutInfo)   ||
+        IsDynamicTensor(constOutputInfo))
+    {
+        return Fail("%s: Dynamic output tensors are not supported %d %d %d %d", __func__,
+                    IsDynamicTensor(constOutputStateOutInfo), IsDynamicTensor(cellStateOutInfo),
+                    IsDynamicTensor(constOutputInfo));
+    }
+
+    bool isSupported = false;
+    FORWARD_LAYER_SUPPORT_FUNC(__func__,
+                               IsQLstmSupported,
+                               data.m_Backends,
+                               isSupported,
+                               inputInfo,
+                               outputStatePrevTimeStepInfo,
+                               cellStatePrevTimeStepInfo,
+                               constOutputStateOutInfo,
+                               cellStateOutInfo,
+                               constOutputInfo,
+                               desc,
+                               paramsInfo);
+    if (!isSupported)
+    {
+        return false;
+    }
+
+    // Add the layer
+    IConnectableLayer* layer = data.m_Network->AddQLstmLayer(desc, params, "QLstm");
+
+    input.Connect(layer->GetInputSlot(0));
+    outputStatePrevTimeStep.Connect(layer->GetInputSlot(1));
+    cellStatePrevTimeStep.Connect(layer->GetInputSlot(2));
+
+    return ( SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 0, *layer, 0, model, data,
+                                                     &constOutputStateOutInfo) &&
+             SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 1, *layer, 1, model, data) &&
+             SetupAndTrackLayerOutputSlot<HalPolicy>(operation, 2, *layer, 2, model, data, &constOutputInfo));
+}
+
 } // armnn_driver namespace
\ No newline at end of file
diff --git a/NnapiSupport.txt b/NnapiSupport.txt
index d5e077b..e3d7c69 100644
--- a/NnapiSupport.txt
+++ b/NnapiSupport.txt
@@ -54,6 +54,7 @@
 PRELU                        (FLOAT32, QUANT8_ASYMM)
 QUANTIZE                     (FLOAT32 (input only), QUANT8_ASYMM (output only))
 QUANTIZED_16BIT_LSTM         (QUANT8_ASYMM)
+QUANTIZED_LSTM               (QUANT8_ASYMM)
 RELU                         (FLOAT32, QUANT8_ASYMM)
 RELU1                        (FLOAT32, QUANT8_ASYMM)
 RELU6                        (FLOAT32, QUANT8_ASYMM)
@@ -74,7 +75,6 @@
 
 Where operations are not supported by the ArmNN Android NN Driver, the driver indicates this to the framework
 appropriately and the framework implements those operations using a CPU implementation.
-
 NOTE: By convention, only those tensor types have been listed above, which are fully supported across all
 ArmNN backends. FLOAT16 input tensors are partially supported on most HAL 1.2 operators on the GpuAcc and
 CpuRef backends, however not on CpuAcc.