论文标题
快速推断粒子物理中FPGA中的促进决策树
Fast inference of Boosted Decision Trees in FPGAs for particle physics
论文作者
论文摘要
我们描述了HLS4ML库中增强决策树的实现，该库可以通过自动转换过程将受过训练的模型转换为FPGA固件。 HLS4ML凭借其完全芯片的实现，对具有极低延迟的增强决策树模型进行推断。对于典型的延迟小于100 ns，该解决方案适用于基于FPGA的实时处理，例如在撞机实验的1级触发系统中。这些发展为物理学家开辟了前景，以在FPGA中部署BDT，以识别喷气机的起源，更好地重建MUON的能量，并可以更好地选择稀有信号过程。
We describe the implementation of Boosted Decision Trees in the hls4ml library, which allows the translation of a trained model into FPGA firmware through an automated conversion process. Thanks to its fully on-chip implementation, hls4ml performs inference of Boosted Decision Tree models with extremely low latency. With a typical latency less than 100 ns, this solution is suitable for FPGA-based real-time processing, such as in the Level-1 Trigger system of a collider experiment. These developments open up prospects for physicists to deploy BDTs in FPGAs for identifying the origin of jets, better reconstructing the energies of muons, and enabling better selection of rare signal processes.
