Speaker
Dylan Rankin
(University of Pennsylvania, Department of Physics and Astronomy)
Description
The use of machine learning (ML) in high energy physics has exploded in the past decade. While it has provide impressive improvements across a broad range of use cases, it has typically been limited to uses with data already collected by experiments. I will discuss the challenges involved with the use of ML on FPGAs in trigger and data acquisition systems in general as well as specific applications of ML on FPGAs in the ATLAS experiment.
