Speaker
Description
For the HL-LHC upgrade of the ATLAS TDAQ system, a heterogeneous computing farm
deploying GPUs and/or FPGAs is under study, together with the use of modern
machine learning algorithms such as Graph Neural Networks (GNNs). We present a
study on the reconstruction of tracks in the ATLAS Inner Tracker using GNNs on
FPGAs for the Event Filter system. We explore each of the steps in a GNN-based
tracking pipeline: graph construction, edge classification using an interaction
network, and segmentation of the graph into track candidates. We investigate
optimizations of the GNN approach that aim to minimize FPGA resources
utilization and maximize throughput while retaining high track reconstruction
efficiency and low fake rates required for the ATLAS Event Filter tracking
system. These studies include model hyperparameter tuning, model pruning and
quantization-aware training, and sequential processing of sub-graphs across the
detector.
