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 topologi...

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Bibliographic Details
Main Authors: Zhao, Chenfeng, Dong, Zehao, Chen, Yixin, Zhang, Xuan, Chamberlain, Roger D.
Format: Conference Proceeding
Language:English
Subjects:
field-programmable gate arrays
graph neural networks
high-level synthesis
Online Access:Request full text
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Summary: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.
ISSN:2576-6996
DOI:10.1109/ICCD58817.2023.00092