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diff --git a/src/armnnSerializer/ArmnnSchema.fbs b/src/armnnSerializer/ArmnnSchema.fbs
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+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+namespace armnnSerializer;
+
+file_identifier "ARMN";
+
+file_extension "armnn";
+
+enum ActivationFunction : byte {
+    Sigmoid = 0,
+    TanH = 1,
+    Linear = 2,
+    ReLu = 3,
+    BoundedReLu = 4,
+    SoftReLu = 5,
+    LeakyReLu = 6,
+    Abs = 7,
+    Sqrt = 8,
+    Square = 9
+}
+
+enum DataType : byte {
+    Float16 = 0,
+    Float32 = 1,
+    QuantisedAsymm8 = 2,
+    Signed32 = 3,
+    Boolean = 4
+}
+
+enum DataLayout : byte {
+    NHWC = 0,
+    NCHW = 1
+}
+
+table TensorInfo {
+    dimensions:[uint];
+    dataType:DataType;
+    quantizationScale:float = 1.0;
+    quantizationOffset:int = 0;
+}
+
+struct Connection {
+    sourceLayerIndex:uint;
+    outputSlotIndex:uint;
+}
+
+table ByteData {
+    data:[byte];
+}
+
+table ShortData {
+    data:[short];
+}
+
+table IntData {
+    data:[int];
+}
+
+table LongData {
+    data:[long];
+}
+
+union ConstTensorData { ByteData, ShortData, IntData, LongData }
+
+table ConstTensor {
+    info:TensorInfo;
+    data:ConstTensorData;
+}
+
+table InputSlot {
+    index:uint;
+    connection:Connection;
+}
+
+table OutputSlot {
+    index:uint;
+    tensorInfo:TensorInfo;
+}
+
+enum LayerType : uint {
+    Addition = 0,
+    Input = 1,
+    Multiplication = 2,
+    Output = 3,
+    Pooling2d = 4,
+    Reshape = 5,
+    Softmax = 6,
+    Convolution2d = 7,
+    DepthwiseConvolution2d = 8,
+    Activation = 9,
+    Permute = 10,
+    FullyConnected = 11
+}
+
+// Base layer table to be used as part of other layers
+table LayerBase {
+    index:uint;
+    layerName:string;
+    layerType:LayerType;
+    inputSlots:[InputSlot];
+    outputSlots:[OutputSlot];
+}
+
+table BindableLayerBase {
+    base:LayerBase;
+    layerBindingId:int;
+}
+
+// Table for each layer defined below
+table ActivationLayer {
+    base:LayerBase;
+    descriptor:ActivationDescriptor;
+}
+
+table ActivationDescriptor {
+    function:ActivationFunction = Sigmoid;
+    a:float;
+    b:float;
+}
+
+table AdditionLayer {
+    base:LayerBase;
+}
+
+table Convolution2dLayer {
+    base:LayerBase;
+    descriptor:Convolution2dDescriptor;
+    weights:ConstTensor;
+    biases:ConstTensor;
+}
+
+table Convolution2dDescriptor {
+    padLeft:uint;
+    padRight:uint;
+    padTop:uint;
+    padBottom:uint;
+    strideX:uint;
+    strideY:uint;
+    biasEnabled:bool = false;
+    dataLayout:DataLayout = NCHW;
+}
+
+table FullyConnectedLayer {
+    base:LayerBase;
+    descriptor:FullyConnectedDescriptor;
+    weights:ConstTensor;
+    biases:ConstTensor;
+}
+
+table FullyConnectedDescriptor {
+    biasEnabled:bool = false;
+    transposeWeightsMatrix:bool = false;
+}
+
+table InputLayer {
+    base:BindableLayerBase;
+}
+
+table MultiplicationLayer {
+    base:LayerBase;
+}
+
+table Pooling2dLayer {
+    base:LayerBase;
+    descriptor:Pooling2dDescriptor;
+}
+
+enum PoolingAlgorithm : byte {
+    Max = 0,
+    Average = 1,
+    L2 = 2
+}
+
+enum OutputShapeRounding : byte {
+    Floor = 0,
+    Ceiling = 1
+}
+
+enum PaddingMethod : byte {
+    IgnoreValue = 0,
+    Exclude = 1
+}
+
+table Pooling2dDescriptor {
+    poolType:PoolingAlgorithm;
+    padLeft:uint;
+    padRight:uint;
+    padTop:uint;
+    padBottom:uint;
+    poolWidth:uint;
+    poolHeight:uint;
+    strideX:uint;
+    strideY:uint;
+    outputShapeRounding:OutputShapeRounding;
+    paddingMethod:PaddingMethod;
+    dataLayout:DataLayout;
+}
+
+table SoftmaxLayer {
+    base:LayerBase;
+    descriptor:SoftmaxDescriptor;
+}
+
+table SoftmaxDescriptor {
+    beta:float;
+}
+
+table DepthwiseConvolution2dLayer {
+    base:LayerBase;
+    descriptor:DepthwiseConvolution2dDescriptor;
+    weights:ConstTensor;
+    biases:ConstTensor;
+}
+
+table DepthwiseConvolution2dDescriptor {
+    padLeft:uint;
+    padRight:uint;
+    padTop:uint;
+    padBottom:uint;
+    strideX:uint;
+    strideY:uint;
+    biasEnabled:bool = false;
+    dataLayout:DataLayout = NCHW;
+}
+
+table OutputLayer {
+    base:BindableLayerBase;
+}
+
+table ReshapeLayer {
+    base:LayerBase;
+    descriptor:ReshapeDescriptor;
+}
+
+table ReshapeDescriptor {
+  targetShape:[uint];
+}
+
+table PermuteLayer {
+    base:LayerBase;
+    descriptor:PermuteDescriptor;
+}
+
+table PermuteDescriptor {
+    dimMappings:[uint];
+}
+
+union Layer {
+    ActivationLayer,
+    AdditionLayer,
+    Convolution2dLayer,
+    DepthwiseConvolution2dLayer,
+    FullyConnectedLayer,
+    InputLayer,
+    MultiplicationLayer,
+    OutputLayer,
+    PermuteLayer,
+    Pooling2dLayer,
+    ReshapeLayer,
+    SoftmaxLayer
+}
+
+table AnyLayer {
+    layer:Layer;
+}
+
+// Root type for serialized data is the graph of the network
+table SerializedGraph {
+    layers:[AnyLayer];
+    inputIds:[uint];
+    outputIds:[uint];
+}
+
+root_type SerializedGraph;