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Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

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arxiv 2012.01563 v1 pith:FPN35VVB submitted 2020-11-30 physics.ins-det cs.LGhep-exphysics.comp-ph

Aneesh Heintz , Vesal Razavimaleki , Javier Duarte , Gage DeZoort , Isobel Ojalvo , Savannah Thais , Markus Atkinson , Mark Neubauer
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Lindsey Gray Sergo Jindariani Nhan Tran Philip Harris Dylan Rankin Thea Aarrestad Vladimir Loncar Maurizio Pierini Sioni Summers Jennifer Ngadiuba Mia Liu Edward Kreinar Zhenbin Wu
This is my paper · ORCID
classification physics.ins-detcs.LGhep-exphysics.comp-ph
keywords neuraltrackingalgorithmschargedfpgagraphimplementationsnetworks
verification ladder T0 review T1 audit T2 compute T3 formal
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We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Geometric GNNs for Charged Particle Tracking at GlueX

    cs.LG 2025-05 conditional novelty 5.0 of 10

    On simulated GlueX Forward Drift Chamber data, a GNN edge classifier reaches 0.9806 segment efficiency at 0.9462 purity versus 0.9119 for the traditional method, with batched GPU inference at 44 microseconds per event...

  2. Transformer networks for Heavy flavor jet tagging

    hep-ph 2024-11 conditional novelty 2.0 of 10

    A review of transformer-based jet tagging that highlights the authors' CA-Mixer network as a state-of-the-art, faster alternative to Particle Transformer.

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