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.

翻译：图神经网络（GNNs），特别是交互网络（INs），在欧洲核子研究中心高亮度大型强子对撞机（HL-LHC）的喷注标记任务中展现出卓越性能。然而，其计算复杂度与不规则内存访问模式对在硬件触发系统中部署于FPGA构成显著挑战，此类系统需满足严格的延迟与资源约束。本研究提出JEDI-linear——一种具有线性计算复杂度的新型GNN架构，通过共享变换与全局聚合机制消除了显式成对交互。为进一步提升硬件效率，我们引入细粒度量化感知训练（支持逐参数位宽优化），并采用基于分布式算术的无乘法器乘累加运算。评估结果表明：与前沿GNN设计相比，基于FPGA的JEDI-linear实现了3.7至11.5倍的延迟降低、最高150倍的初始间隔降低以及最高6.2倍的LUT使用量降低，同时提供更高模型精度并完全无需DSP模块。这是首个延迟低于60纳秒的交互式GNN，目前满足HL-LHC CMS一级触发系统的使用要求。本工作通过实现实时环境中精准、可扩展且资源高效的GNN推理，推动了下一代触发系统的发展。我们开源的模板将进一步提升科学应用领域的可复现性与广泛适用性。