Kalman Filter track reconstruction on FPGAs for acceleration of the High Level Trigger of the CMS experiment at the HL-LHC
Kalman Filter track reconstruction on FPGAs for acceleration of the High Level Trigger of the CMS experiment at the HL-LHC
复制标题
FPGA 上的卡尔曼滤波器轨迹重建，用于加速 HL-LHC CMS 实验的高级触发
作者：
S. Summers;A. Rose
CMS实验中的航迹重建使用组合卡尔曼滤波器。算法计算时间与堆积成指数级，这将对高亮度LHC的高电平触发器造成问题。已经广泛用于硬件触发器的FPGA正越来越广泛地用于计算加速。FPGA加速器集高性能、高能效、可预测和低延迟于一身，是高能物理领域的一项有趣技术。这里，显示了将CMS轨道重建移植到Maxeler Technologies的数据流引擎的进展，使用其高级语言MaxJ编程。性能相比，CPU，并提出了进一步的步骤，以优化架构。
Track reconstruction at the CMS experiment uses the Combinatorial Kalman Filter. The algorithm computation time scales exponentially with pileup, which will pose a problem for the High Level Trigger at the High Luminosity LHC. FPGAs, which are already used extensively in hardware triggers, are becoming more widely used for compute acceleration. With a combination of high performance, energy efficiency, and predictable and low latency, FPGA accelerators are an interesting technology for high energy physics. Here, progress towards porting of the CMS track reconstruction to Maxeler Technologies’ Dataflow Engines is shown, programmed with their high level language MaxJ. The performance is compared to CPUs, and further steps to optimise for the architecture are presented.