  The determination of charged particle trajectories in collisions at the CERN
Large Hadron Collider (LHC) is an important but challenging problem, especially
in the high interaction density conditions expected during the future
high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a
type of geometric deep learning algorithm that has successfully been applied to
this task by embedding tracker data as a graph -- nodes represent hits, while
edges represent possible track segments -- and classifying the edges as true or
fake track segments. However, their study in hardware- or software-based
trigger applications has been limited due to their large computational cost. In
this paper, we introduce an automated translation workflow, integrated into a
broader tool called $\texttt{hls4ml}$, for converting GNNs into firmware for
field-programmable gate arrays (FPGAs). We use this translation tool to
implement GNNs for charged particle tracking, trained using the TrackML
challenge dataset, on FPGAs with designs targeting different graph sizes, task
complexites, and latency/throughput requirements. This work could enable the
inclusion of charged particle tracking GNNs at the trigger level for HL-LHC
experiments.
