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

TL;DR

This paper compares FPGA and GPU performance for machine learning-based track reconstruction in high-energy physics, highlighting FPGA's potential for high-throughput, low-latency inference with lower power consumption.

Contribution

It provides a novel benchmarking of FPGA and GPU inference performance for ML models in LHCb's track reconstruction pipeline, demonstrating FPGA's advantages.

Findings

FPGAs achieve comparable throughput to GPUs for ML inference.

FPGAs consume significantly less power than GPUs.

FPGAs enable high-throughput, low-latency inference without FPGA expertise.

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.