GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis

We present GNNHLS, an open-source framework to comprehensively evaluate GNN inference acceleration on FPGAs via HLS, containing a software stack for data generation and baseline deployment and FPGA implementations of 6 well-tuned GNN HLS kernels. Evaluating on 4 graph datasets with distinct topologies and scales, the results show that GNNHLS achieves up to 50.8× speedup and 423× energy reduction relative to the CPU baselines. Compared with the GPU baselines, GNNHLS achieves up to 5.16× speedup and 74.5× energy reduction.