用于高能物理的基于FPGA中提升决策树的纳秒级机器学习事件分类
Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
AI总结：
本文提出在FPGA上实现提升决策树（BDT）的纳秒级事件分类方法，通过重构BDT布局和参数配置，以低资源利用率实现约10 ns延迟，适用于高能物理实时触发系统。
摘要：
我们提出了一种在现场可编程门阵列（FPGA）上使用称为提升决策树（BDT）的机器学习/人工智能方法进行分类的新颖实现。该固件实现使用四个输入变量进行二分类，需要100棵最大深度为4的训练树，在我们的设置中给出约10 ns的延迟值，与100至320 MHz的时钟速度无关。低时序值是通过重构BDT布局并重新配置其参数来实现的。在我们的设置中，FPGA资源利用率也保持在0.01%至0.2%的低范围内。一个名为fwXmachina的软件包实现了这一实现。我们的目标用户是高能物理实验中基于定制电子触发系统的专家，或任何需要以最低延迟值进行实时事件分类决策的人。考虑了高能物理中的两个问题：电子与光子的分离，以及矢量玻色子聚变产生的希格斯玻色子的选择与多喷注过程的排除。
英文摘要：
We present a novel implementation of classification using the machine learning / artificial intelligence method called boosted decision trees (BDT) on field programmable gate arrays (FPGA). The firmware implementation of binary classification requiring 100 training trees with a maximum depth of 4 using four input variables gives a latency value of about 10 ns, independent of the clock speed from 100 to 320 MHz in our setup. The low timing values are achieved by restructuring the BDT layout and reconfiguring its parameters. The FPGA resource utilization is also kept low at a range from 0.01% to 0.2% in our setup. A software package called fwXmachina achieves this implementation. Our intended user is an expert of custom electronics-based trigger systems in high energy physics experiments or anyone that needs decisions at the lowest latency values for real-time event classification. Two problems from high energy physics are considered, in the separation of electrons vs. photons and in the selection of vector boson fusion-produced Higgs bosons vs. the rejection of the multijet processes.
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