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

2025/09/30 by Errico, Martino, Fiacco, Davide, Giagu, Stefano +3
#FOS: Physical sciences #High Energy Physics - Experiment (hep-ex)

paper · doi:10.48550/arxiv.2509.26419

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.

Citations

• Fast inference of deep neural networks in FPGAs for particle physics

Related

• Using AI on FPGAs for the CMS Overlap Muon Track Finder for the HL-LHC
• Design and deployment of a fast neural network for measuring the properties of muons originating from displaced vertices in the CMS Endcap Muon Track Finder
• Analysis of Hardware Synthesis Strategies for Machine Learning in Collider Trigger and Data Acquisition
• Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures
• Rapid Inference of Logic Gate Neural Networks for Anomaly Detection in High Energy Physics