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

• URL: http://arxiv.org/abs/2502.02304v3
• Date: Sun, 16 Feb 2025 20:13:26 GMT
• Title: Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb
• Authors: Fotis I. Giasemis, Vladimir Lončar, Bertrand Granado, Vladimir Vava Gligorov,
• Abstract summary: Increasing luminosity and 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 charged particle tracks.
• Score: 28.573896827794773
• License:
• 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$\unicode{x2013}$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.

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