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Facing increasing data challenges at current and future HEP experiments, there is a growing demand for fast and efficient Machine Learning (ML) techniques deployed close to the detector. In this seminar, we explore the application of Fast ML in accelerating and optimizing ML algorithms for real-time analysis, triggering, and data reduction, focussing on two projects in use at CERN experiments: hls4ml and conifer. These tools offer a novel approach transforming ML models into efficient hardware descriptions suitable for implementation on field-programmable gate arrays (FPGAs) and other hardware accelerators. We present the state of the art techniques for compressing ML models for fast and lightweight inference. We highlight case studies and experimental results that have leveraged these tools and techniques to improve performance such as particle identification, event classification, and anomaly detection. Finally, we present key future development directions.
Coffee will be served at 10:30.
Michael Campbell