Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb
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arXiv
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| Format: | Preprint |
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2025
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| author | Giasemis, Fotis I. Lončar, Vladimir Granado, Bertrand Gligorov, Vladimir Vava |
| author_facet | Giasemis, Fotis I. Lončar, Vladimir Granado, Bertrand Gligorov, Vladimir Vava |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02304 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb Giasemis, Fotis I. Lončar, Vladimir Granado, Bertrand Gligorov, Vladimir Vava High Energy Physics - Experiment Distributed, Parallel, and Cluster Computing Machine Learning Instrumentation and Detectors 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. |
| title | Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb |
| topic | High Energy Physics - Experiment Distributed, Parallel, and Cluster Computing Machine Learning Instrumentation and Detectors |
| url | https://arxiv.org/abs/2502.02304 |