  The determination of charged particle trajectories (tracking) in collisions
at the CERN Large Hadron Collider (LHC) is one of the most important aspects
for event reconstruction at hadron colliders. This is especially true in the
high conditions expected during the future high-luminosity phase of the LHC
(HL-LHC) where the number of interactions per beam crossing will increase by a
factor of five. Deep learning algorithms have been successfully applied to this
task for offline applications. However, their study in hardware-based trigger
applications has been limited . In this paper, we study different algorithms
for two different steps of tracking and show that such algorithms can be run on
field-programmable gate arrays (FPGAs).
