TrackGNN: A Highly Parallelized and Self-Adaptive GNN Accelerator for Track Reconstruction on FPGAs

Shuyang Li, Hanqing Zhang, Ruiqi Chen, Bruno da Silva, Giorgian Borca-Tasciuc, Dantong Yu, Cong Hao

Abstract: Real-time track reconstruction in high energy physics imposes stringent latency constraints, hindering the deployment of graph neural networks (GNNs) on general-purpose platforms. We present TrackGNN11https//github.com/silvenachen/TrackGNN, an open-sourced GNN accelerator for track reconstruction. Using a dataflow architecture with multiple parallelism and a self-adaptive renaming mechanism, TrackGNN shows 27.6× speedup over CPUs, up to 101.1× over GPUs, and 5.7× over an FPGA overlay. Compared with FlowGNN, the renaming mechanism also reduces end-to-end latency by 1.12-1.16× with negligible resource overhead.