Ultrafast Machine Learning Inference in FPGAs at the LHC
by
U3-01
University of Milan Bicocca
At the CERN Large Hadron Collider, protons are brought to collide hundreds of millions of times per second. The collision debris allows us to study the fundamental building blocks of the universe and look for hints of new forces and particles. The vast majority of the collision data are immediately discarded by a real-time event filtering system due to storage and computational limitations. While most of these data are uninterresting, signals of new physics might be inadvertendly thrown away in the process. The first stage of this event filtering system consists of hundreds of field-programmable gate arrays (FPGAs), tasked with rejecting over 98% of the proton collisions within a few microseconds. With the start of High Luminosity LHC in 2029, a more granular detector and more particles per collision will increase the event complexity significantly, and ultimately require the FPGA farm to process an amount of data comparable to 5% of the total internet traffic.
In this talk, I will discuss how we are using real-time Machine Learning on FPGAs to process and filter this enormous amount of data in the pursuit of new physics. I will discuss methods and tools for designing low-latency, efficient algorithms that run on FPGA hardware and, finally, I will explore how real-time anomaly detection can be used to process and collect data in a way never before performed at colliders.