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复杂的触发与采集系统，以高效保留用于进一步研究的感兴趣碰撞事件。利用FPGA卡的异构计算可能成为欧洲核子研究中心大型强子对撞机即将开展的高亮度计划中触发策略的新兴技术。在此背景下，我们提出两种机器学习算法，用于选择中性长寿命粒子在探测器体积内衰变的事件，并研究其精度以及在使用商用Xilinx FPGA加速卡进行加速时的推理时间。推理时间还与基于CPU和GPU的硬件设置进行了对比。所提新算法经证明在所考虑的基准物理场景中高效运行，且在FPGA卡上加速时精度未出现下降。结果表明：所有测试的架构均满足二级触发系统的延迟要求，并且利用加速器技术对粒子物理碰撞进行实时处理是一个值得进一步研究的广阔领域，尤其是对于具有大量可训练参数的机器学习模型而言。