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Title: Graph Neural Networks for Charged Particle Tracking on FPGAs

Journal Article · · Frontiers in Big Data
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The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph—nodes represent hits, while edges represent possible track segments—and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml , for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.

Sponsoring Organization:
USDOE
Grant/Contract Number:
SC0007968; SC0021187
OSTI ID:
1859670
Journal Information:
Frontiers in Big Data, Journal Name: Frontiers in Big Data Vol. 5; ISSN 2624-909X
Publisher:
Frontiers Media SACopyright Statement
Country of Publication:
Switzerland
Language:
English

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