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.

本研究对于 FPGA 卡驱动的机器学习算法的使用进行探讨，并证明在高亮度程序触发策略和事件选择中，这种算法具有较高的准确性和较低的推理时间。