Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge as a trending technology for the triggering strategy of the upcoming high-luminosity program of the Large Hadron Collider at CERN. In this context, we present two machine-learning algorithms for selecting events where neutral long-lived particles decay within the detector volume studying their accuracy and inference time when accelerated on commercially available Xilinx FPGA accelerator cards. The inference time is also confronted with a CPU- and GPU-based hardware setup. The proposed new algorithms are proven efficient for the considered benchmark physics scenario and their accuracy is found to not degrade when accelerated on the FPGA cards. The results indicate that all tested architectures fit within the latency requirements of a second-level trigger farm and that exploiting accelerator technologies for real-time processing of particle-physics collisions is a promising research field that deserves additional investigations, in particular with machine-learning models with a large number of trainable parameters.

翻译：实验粒子物理学需要一套精密的触发与采集系统，能够高效保留有价值的碰撞事件以供进一步研究。采用现场可编程门阵列（FPGA）的异构计算技术，正逐步成为欧洲核子研究中心（CERN）大型强子对撞机未来高亮度运行阶段触发策略的前沿方向。在此背景下，我们提出了两种机器学习算法，用于筛选中性长寿命粒子在探测器体积内衰变的事件，并评估了它们在商用赛灵思FPGA加速卡上加速运行时的准确性与推理时间。同时，我们还将推理时间与基于CPU和GPU的硬件方案进行了对比。实验证明，所提出的新算法在基准物理场景中表现高效，且在FPGA卡上加速后准确率未出现下降。结果表明，所有测试架构均满足二级触发系统的延迟需求；利用加速器技术实现粒子物理碰撞的实时处理是一个值得深入探索的研究方向，尤其适用于具有大量可训练参数的机器学习模型。