Speaker
Description
The upcoming High Luminosity phase of the Large Hadron Collider will require significant advancements in real-time data processing to handle the increased event rates and maintain high-efficiency trigger decisions. In this work, we extend our previous studies on deploying compressed deep neural networks on FPGAs for high-energy physics applications by exploring the acceleration of graph neural networks for fast inference in the ATLAS muon trigger system.
Graph-based architectures offer a natural way to represent and process detector hits while preserving spatial and topological information, making them particularly suitable for muon reconstruction in a noisy and sparse environment. This work contributes to the broader goal of integrating AI-driven solutions into HEP triggering systems and represents a step forward in realizing fast, hardware-optimized, graph-based inference in experimental physics.
| AI keywords | fast-inference:FPGA:trigger:LHC |
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