JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs

TL;DR

JEDI-linear is a novel, resource-efficient GNN architecture optimized for FPGA deployment, enabling fast, accurate jet tagging for high-energy physics experiments with significantly reduced latency and resource usage.

Contribution

It introduces a linear-complexity GNN architecture with quantization and multiplier-free operations, achieving superior FPGA performance and accuracy over existing designs.

Findings

Achieves 3.7 to 11.5 times lower latency on FPGA

Up to 150 times lower initiation interval and 6.2 times lower LUT usage

Meets sub-60 ns latency requirement for HL-LHC trigger systems

Abstract

Graph Neural Networks (GNNs), particularly Interaction Networks (INs), have shown exceptional performance for jet tagging at the CERN High-Luminosity Large Hadron Collider (HL-LHC). However, their computational complexity and irregular memory access patterns pose significant challenges for deployment on FPGAs in hardware trigger systems, where strict latency and resource constraints apply. In this work, we propose JEDI-linear, a novel GNN architecture with linear computational complexity that eliminates explicit pairwise interactions by leveraging shared transformations and global aggregation. To further enhance hardware efficiency, we introduce fine-grained quantization-aware training with per-parameter bitwidth optimization and employ multiplier-free multiply-accumulate operations via distributed arithmetic. Evaluation results show that our FPGA-based JEDI-linear achieves 3.7 to 11.5 times lower latency, up to 150 times lower initiation interval, and up to 6.2 times lower LUT usage compared to state-of-the-art GNN designs while also delivering higher model accuracy and eliminating the need for DSP blocks entirely. This is the first interaction-based GNN to achieve less than 60~ns latency and currently meets the requirements for use in the HL-LHC CMS Level-1 trigger system. This work advances the next-generation trigger systems by enabling accurate, scalable, and resource-efficient GNN inference in real-time environments. Our open-sourced templates will further support reproducibility and broader adoption across scientific applications.