Thursday 20 March 2025
The quest for faster and more efficient processing power in high-energy physics experiments has led researchers to explore new frontiers. In a recent study, scientists have turned to field-programmable gate arrays (FPGAs), often used in data centers and supercomputers, to accelerate machine learning algorithms.
Traditionally, general-purpose graphics processing units (GPUs) have been the go-to choice for accelerating machine learning tasks. However, FPGAs offer a unique set of advantages that make them an attractive alternative. For one, they consume significantly less power than GPUs, making them more suitable for applications where energy efficiency is crucial.
The researchers used a library called HLS4ML to convert a trained machine learning model into FPGA-optimized code. This allowed them to deploy the model on a PYNQ-Z2 board, which features a Xilinx Zynq-7020 FPGA. The experiment involved implementing an embedding multilayer perceptron (MLP), a crucial component of a graph neural network-based track reconstruction pipeline for the VELO detector at the Large Hadron Collider.
The results were impressive: the FPGA implementation achieved a throughput of approximately 1.2 million inferences per second, comparable to the performance of a GPU running an INT8-precision implementation. Moreover, the FPGA implementation consumed only about 60% of the power used by the GPU counterpart.
To further explore the potential of FPGAs for machine learning acceleration, the researchers also implemented the MLP on two Alveo accelerator cards: the U50 and U250. These high-end cards are designed for data center applications and offer significant processing capabilities. The results showed that the FPGA implementations outperformed the GPU implementation in terms of energy efficiency, with the U50 card achieving a throughput of 550 events per second while consuming only 75 watts.
The implications of this research are far-reaching. As high-energy physics experiments continue to generate vast amounts of data, the need for efficient and scalable processing solutions becomes increasingly pressing. FPGAs offer a promising solution, allowing researchers to accelerate machine learning tasks while reducing energy consumption.
This study highlights the potential of FPGAs as viable alternatives for high-throughput applications in particle physics, particularly when energy efficiency is a key consideration. As researchers continue to explore new frontiers in machine learning and high-energy physics, the use of FPGAs will likely play an increasingly important role in the development of next-generation processing solutions.
Cite this article: “Accelerating Machine Learning with Field-Programmable Gate Arrays in High-Energy Physics”, The Science Archive, 2025.
Machine Learning, Fpgas, High-Energy Physics, Particle Physics, Data Centers, Supercomputers, Gpus, Power Consumption, Energy Efficiency, Accelerators
