Co-processor Meeting (Type B)

Friday 14 Feb 2025, 10:00 → 11:00 America/Chicago

Yuan-Tang Chou (University of Washington (US)), Yongbin Feng (Texas Tech University (US)), Ethan Marx (MIT)

1 Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb

Abstract: In high-energy physics, the increasing luminosity and detector granularity at the Large Hadron Collider are driving the need for more efficient data processing solutions. Machine Learning has emerged as a promising tool for reconstructing charged particle tracks, due to its potentially linear computational scaling with detector hits. The recent implementation of a graph neural network-based track reconstruction pipeline in the first level trigger of the LHCb experiment on GPUs serves as a platform for comparative studies between computational architectures in the context of high-energy physics. This paper presents a novel comparison of the throughput of ML model inference between FPGAs and GPUs, focusing on the first step of the track reconstruction pipeline—an implementation of a multilayer perceptron. Using HLS4ML for FPGA deployment, we benchmark its performance against the GPU implementation and demonstrate the potential of FPGAs for high-throughput, low-latency inference without the need for an expertise in FPGA development and while consuming significantly less power.

https://arxiv.org/pdf/2502.02304

Speaker: Fotis Giasemis (Centre National de la Recherche Scientifique (FR))

giasemis-fastml-seminar.pdf