Machine Learning Acceleration for Fast Triggers

Modern particle physics experiments generate vast amounts of data; far more than can possibly be stored. Experiments such as CMS and DUNE have built fast, complex, data processing systems, that can identify interesting events in the data, and save them for analysis. However these systems have limitations which can impact the precision of the measurements made by the experiment. Machine learning algorithms offer an exciting possibility to improve the performance of the data selection (or "trigger") systems. These algorithms are not typically fast enough for particle physics experiments, but a new generation of fast, programmable, processing devices may speed them up sufficiently to be useful. In this project we will evaluate the suitability of latest generation devices for these experiments, as well as developing machine learning algorithms which are fast enough, and performant enough, to improve the physics reach of CMS and DUNE.