diff --git a/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp b/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp
new file mode 100644
index 0000000..9c095d6
--- /dev/null
+++ b/src/backends/tosaCommon/operatorMappings/Conv2dOperator.cpp
@@ -0,0 +1,123 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "Conv2dOperator.hpp"
+
+TosaSerializationBasicBlock* ConvertConv2dToTosaOperator(const Layer* layer,
+                                                         const std::vector<const TensorInfo*>& inputs,
+                                                         const std::vector<const TensorInfo*>& outputs,
+                                                         const Convolution2dDescriptor* conv2dDescriptor)
+{
+    std::vector<std::string> inputNames;
+    std::string outputName = std::string("output0_");
+    std::string blockName  = std::string("Op_CONV2D_block_") + GetUniqueTosaMappingID();
+
+    // Set input names for validation purposes only.
+    if(layer == nullptr)
+    {
+        inputNames.emplace_back("input0_");
+        inputNames.emplace_back("input1_");
+        if(conv2dDescriptor->m_BiasEnabled)
+        {
+            inputNames.emplace_back("input2_");
+        }
+    }
+    else
+    {
+        // If a layer is present then the block will be used for execution, so input and output names need to be
+        // determined using the previous and following layers so the graph is connected correctly.
+        // For validation this doesn't matter.
+        for (uint32_t i = 0; i < inputs.size(); ++i)
+        {
+            // Get the layer connected to the input slot and determine unique layer name.
+            Layer& connectedLayer = layer->GetInputSlot(i).GetConnectedOutputSlot()->GetOwningLayer();
+
+            std::string inputName = GenerateUniqueName(connectedLayer, i);
+            inputNames.push_back(inputName);
+        }
+
+        // Get the layer connected to the output slot and determine unique layer name.
+        Layer& connectedLayer = layer->GetOutputSlot().GetConnection(0)->GetOwningLayer();
+
+        outputName = GenerateUniqueName(connectedLayer, 0);
+    }
+
+    std::vector<TosaSerializationTensor*> tensors;
+    std::vector<TosaSerializationOperator*> operators;
+
+    // Setup input Tensor
+    std::vector<int32_t> inputShape0 = GetTosaTensorShape(inputs[0]->GetShape());
+    DType inputDType0 = ArmNNToDType(inputs[0]->GetDataType());
+
+    tensors.push_back(new TosaSerializationTensor(inputNames[0], inputShape0, inputDType0, {}));
+
+    // Only add input tensors if weights and bias are not constant or if running validation.
+    // Constant tensors will be created in the ConvertConstantToTosaOperator function.
+    if(!inputs[1]->IsConstant() || layer == nullptr)
+    {
+        std::vector<int32_t> inputShape1 = GetTosaTensorShape(inputs[1]->GetShape());
+        DType inputDType1 = ArmNNToDType(inputs[1]->GetDataType());
+
+        tensors.push_back(new TosaSerializationTensor(inputNames[1], inputShape1, inputDType1, {}));
+    }
+
+    if(conv2dDescriptor->m_BiasEnabled)
+    {
+        if(!inputs[2]->IsConstant() || layer == nullptr)
+        {
+            std::vector<int32_t> inputShape2 = GetTosaTensorShape(inputs[2]->GetShape());
+            DType inputDType2 = ArmNNToDType(inputs[2]->GetDataType());
+
+            tensors.push_back(new TosaSerializationTensor(inputNames[2], inputShape2, inputDType2, {}));
+        }
+    }
+    else
+    {
+        // If bias is disabled, create a constant bias of 0 as three inputs are required.
+        std::string constantName = std::string("constant_") + GetUniqueTosaMappingID();
+
+        operators.push_back(new TosaSerializationOperator(Op_CONST, Attribute_NONE, nullptr, {}, {constantName}));
+
+        std::vector<uint8_t> uint8Data;
+        std::vector<float> data = { 0.0 };
+
+        TosaSerializationHandler::ConvertF32toU8(data, uint8Data);
+
+        tensors.push_back(new TosaSerializationTensor(constantName, {1}, DType_FP32, uint8Data));
+        inputNames.emplace_back(constantName);
+    }
+
+    // Setup Output Tensor
+    std::vector<int32_t> outputShape0 = GetTosaTensorShape(outputs[0]->GetShape());
+    DType outputDType0 = ArmNNToDType(outputs[0]->GetDataType());
+
+    tensors.push_back(new TosaSerializationTensor(outputName, outputShape0, outputDType0, {}));
+
+    // Set up CONV2D operator
+    std::vector<int> pad = {static_cast<int>(conv2dDescriptor->m_PadTop),
+                            static_cast<int>(conv2dDescriptor->m_PadBottom),
+                            static_cast<int>(conv2dDescriptor->m_PadLeft),
+                            static_cast<int>(conv2dDescriptor->m_PadRight)};
+    std::vector<int> stride = {static_cast<int>(conv2dDescriptor->m_StrideY),
+                               static_cast<int>(conv2dDescriptor->m_StrideX)};
+    std::vector<int> dilation = {static_cast<int>(conv2dDescriptor->m_DilationY),
+                                 static_cast<int>(conv2dDescriptor->m_DilationX)};
+    TosaConvAttribute attribute(pad, dilation, stride, 0, 0, ArmNNToDType(inputs[0]->GetDataType()));
+
+    auto* op = new TosaSerializationOperator(Op_CONV2D,
+                                             Attribute_ConvAttribute,
+                                             &attribute,
+                                             inputNames,
+                                             {outputName});
+    operators.push_back(op);
+
+    // operatorInputNames/operatorOutputNames ends up being the same as
+    // blockInputNames/blockOutputNames for one-to-one ArmNN to TOSA mappings
+    return new TosaSerializationBasicBlock(blockName,     // name
+                                           operators,     // operators
+                                           tensors,       // tensors
+                                           inputNames,    // inputs
+                                           {outputName}); // outputs
+}
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