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diff --git a/samples/DynamicSample.cpp b/samples/DynamicSample.cpp
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+++ b/samples/DynamicSample.cpp
@@ -0,0 +1,80 @@
+//
+// Copyright © 2020 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include <armnn/INetwork.hpp>
+#include <armnn/IRuntime.hpp>
+#include <armnn/Utils.hpp>
+#include <armnn/Descriptors.hpp>
+
+#include <iostream>
+
+/// A simple example of using the ArmNN SDK API. In this sample, the users single input number is multiplied by 1.0f
+/// using a fully connected layer with a single neuron to produce an output number that is the same as the input.
+int main()
+{
+    using namespace armnn;
+
+    // Construct ArmNN network
+    armnn::NetworkId networkIdentifier;
+    INetworkPtr myNetwork = INetwork::Create();
+
+    IConnectableLayer* input0 = myNetwork->AddInputLayer(0);
+    IConnectableLayer* input1 = myNetwork->AddInputLayer(1);
+    IConnectableLayer* add    = myNetwork->AddAdditionLayer();
+    IConnectableLayer* output = myNetwork->AddOutputLayer(0);
+
+    input0->GetOutputSlot(0).Connect(add->GetInputSlot(0));
+    input1->GetOutputSlot(0).Connect(add->GetInputSlot(1));
+    add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
+
+    TensorInfo tensorInfo(TensorShape({2, 1}), DataType::Float32);
+    input0->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+    input1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+    add->GetOutputSlot(0).SetTensorInfo(tensorInfo);
+
+    // Create ArmNN runtime
+    IRuntime::CreationOptions options; // default options
+    armnn::IRuntimePtr run(armnn::IRuntime::Create(options));
+
+    // Optimise ArmNN network
+    armnn::IOptimizedNetworkPtr optNet = Optimize(*myNetwork, {"SampleDynamic"}, run->GetDeviceSpec());
+    if (!optNet)
+    {
+        // This shouldn't happen for this simple sample, with reference backend.
+        // But in general usage Optimize could fail if the hardware at runtime cannot
+        // support the model that has been provided.
+        std::cerr << "Error: Failed to optimise the input network." << std::endl;
+        return 1;
+    }
+
+    // Load graph into runtime
+    run->LoadNetwork(networkIdentifier, std::move(optNet));
+
+    // input data
+    std::vector<float> input0Data
+        {
+            5.0f, 3.0f
+        };
+    std::vector<float> input1Data
+        {
+            10.0f, 8.0f
+        };
+    std::vector<float> outputData(2);
+
+    InputTensors inputTensors
+        {
+            {0,armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), input0Data.data())},
+            {1,armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), input1Data.data())}
+        };
+    OutputTensors outputTensors
+        {
+            {0,armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0), outputData.data())}
+        };
+
+    // Execute network
+    run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
+
+    std::cout << "Addition operator result is {" << outputData[0] << "," << outputData[1] << "}" << std::endl;
+    return 0;
+}