Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC

Martino Errico; Davide Fiacco; Stefano Giagu; Giuliano Gustavino; Valerio Ippolito; Graziella Russo

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arXiv · 2509.26419

Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC

Martino Errico·Davide Fiacco·Stefano Giagu·Giuliano Gustavino·Valerio Ippolito·Graziella Russo

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Abstract

The High-Luminosity LHC (HL-LHC) will reach luminosities up to 7 times higher than the previous run, yielding denser events and larger occupancies. Next generation trigger algorithms must retain reliable selection within a strict latency budget. This work explores machine-learning approaches for future muon triggers, using the ATLAS Muon Spectrometer as a benchmark. A Convolutional Neural Network (CNN) is used as a reference, while a Graph Neural Network (GNN) is introduced as a natural model for sparse detector data. Preliminary single-track studies show that GNNs achieve high efficiency with compact architectures, an encouraging result in view of FPGA deployment.

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Martino Errico, Davide Fiacco, Stefano Giagu, Giuliano Gustavino, Valerio Ippolito, Graziella Russo. 2025-09-30. Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC. https://arxiv.org/abs/2509.26419

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