Graph neural networks (GNNs) have become an increasingly popular research topic. Given its advantages in exploiting the graph data structure, any graph-related problems can be using the graph neural networks like medicine discovery, recommending systems, and quantum chemistry modeling. However, accelerating the training and inferencing of GNNs are limited by problems like Massive irregular memory access and intensive computation for embedding transformation. Due to these characteristics of graph neural networks, the commonly used hardware accelerators (GPUs and CPUs) are not ideal for accelerating GNNs. In this project, we focus on one of the specific variants of GNN, named Graph Convolutional Network (GCN). Furthermore, we aim to implement the GCN inference model on FPGA. The primary approach to implementing this project is Vitis HLS, a high-level synthesis tool that can convert the C code into RTL code running on FPGA.
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