Speaker
Description
The next phase of high energy particle physics research at CERN will
involve the High-Luminosity Large Hadron Collider (HL-LHC). In preparation for
this phase, the ATLAS Trigger and Data AcQuisition (TDAQ) system will undergo
upgrades to the online software tracking capabilities. Studies are underway to
assess a heterogeneous computing farm deploying GPUs and/or FPGAs, 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 new
all-silicon 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 regions of the
detector as graphs.
