FPGAN: An FPGA Accelerator for Graph Attention Networks With Software and Hardware Co-Optimization

IEEE Access, (2020): 171608-171620

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Abstract:

The Graph Attention Networks (GATs) exhibit outstanding performance in multiple authoritative node classification benchmark tests (including transductive and inductive). The purpose of this research is to implement an FPGA-based accelerator called FPGAN for graph attention networks that achieves significant improvement on performance and ...More

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