  This study focuses on implementing a real-time control system for a particle
accelerator facility that performs high energy physics experiments. A critical
operating parameter in this facility is beam loss, which is the fraction of
particles deviating from the accelerated proton beam into a cascade of
secondary particles. Accelerators employ a large number of sensors to monitor
beam loss. The data from these sensors is monitored by human operators who
predict the relative contribution of different sub-systems to the beam loss.
Using this information, they engage control interventions. In this paper, we
present a controller to track this phenomenon in real-time using edge-Machine
Learning (ML) and support control with low latency and high accuracy. We
implemented this system on an Intel Arria 10 SoC. Optimizations at the
algorithm, high-level synthesis, and interface levels to improve latency and
resource usage are presented. Our design implements a neural network, which can
predict the main source of beam loss (between two possible causes) at speeds up
to 575 frames per second (fps) (average latency of 1.74 ms). The practical
deployed system is required to operate at 320 fps, with a 3ms latency
requirement, which has been met by our design successfully.
