论文标题
纳秒机器学习回归，具有FPGA的深度促进决策树的高能量物理学
Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics
论文作者
论文摘要
我们提出了称为“增强决策树”的机器学习 /人工智能方法的新颖应用，以估计现场可编程门阵列（FPGA）上的物理量。软件包FWXMachina具有一个名为“并行决策路径”的新体系结构，该架构允许具有任意数量的输入变量的深度决策树。它还具有一种新的优化方案，用于为每个输入变量使用不同数量的位，该方案可产生最佳物理结果和超高效率的FPGA资源利用。考虑了大型强子对撞机（LHC）质子碰撞高能物理学的问题。在高光度LHC（HL-LHC）实验的第一级触发系统处缺失横向动量（ETMISS）的估计，该实验的简化检测器由Delphes建模，用于基准并表征固件性能。使用八个输入变量（16位精度）的最大深度最大深度为10的固件实现，可延迟值为O（10）NS，独立于时钟速度，而O（0.1）的可用FPGA资源的o（0.1）％而无需使用数字信号处理器。
We present a novel application of the machine learning / artificial intelligence method called boosted decision trees to estimate physical quantities on field programmable gate arrays (FPGA). The software package fwXmachina features a new architecture called parallel decision paths that allows for deep decision trees with arbitrary number of input variables. It also features a new optimization scheme to use different numbers of bits for each input variable, which produces optimal physics results and ultraefficient FPGA resource utilization. Problems in high energy physics of proton collisions at the Large Hadron Collider (LHC) are considered. Estimation of missing transverse momentum (ETmiss) at the first level trigger system at the High Luminosity LHC (HL-LHC) experiments, with a simplified detector modeled by Delphes, is used to benchmark and characterize the firmware performance. The firmware implementation with a maximum depth of up to 10 using eight input variables of 16-bit precision gives a latency value of O(10) ns, independent of the clock speed, and O(0.1)% of the available FPGA resources without using digital signal processors.
