Name: Co-processor Meeting (Type B)
Start: 2025-02-14T10:00:00-06:00
End: 2025-02-14T11:00:00-06:00
Location: No location set

- 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