Speaker
Description
The High-Luminosity Large Hadron Collider (HL-LHC) at CERN marks a
new era for high-energy particle physics, demanding significant
upgrades to the ATLAS Trigger and Data Acquisition (TDAQ) system.
Central to these upgrades is the enhancement of online software
tracking capabilities to meet the unprecedented data rates and
complexity of HL-LHC operations. This study investigates the
deployment of Graph Neural Networks (GNNs) on Field-Programmable
Gate Arrays (FPGAs) within the Event Filter system of the ATLAS
experiment. Focusing on the reconstruction of tracks in the new
all-silicon ATLAS Inner Tracker, we detail a GNN-based tracking
pipeline comprising graph construction, edge classification via
interaction networks, and segmentation into track candidates. Key
optimizations, including model hyperparameter tuning, pruning,
quantization-aware training, and sequential processing of detector
regions, are explored to reduce FPGA resource utilization and
maximize throughput. Our results demonstrate the potential of this
approach to achieve high tracking efficiency and low fake rates,
aligning with the stringent requirements of the ATLAS Event Filter
system for HL-LHC operations.
