Nanosecond machine learning regression with deep boosted decision trees in FPGA for high energy physics

Abstract

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.

27 pages, 14 figures, 5 tables

References in corpus (17)

• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• An Introduction to PYTHIA 8.2
• Pileup subtraction using jet areas
• The CMS trigger system
• The Catchment Area of Jets
• Performance of missing transverse momentum reconstruction in proton-proton collisions at $\sqrt{s}=$ 13 TeV using the CMS detector
• Search for new phenomena in events with an energetic jet and missing transverse momentum in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Performance of the CMS missing transverse energy reconstruction in pp data at sqrt(s) = 8 TeV
• Energy Linearity and Resolution of the ATLAS Electromagnetic Barrel Calorimeter in an Electron Test-Beam
• Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference
• Graph Neural Networks for Charged Particle Tracking on FPGAs
• Performance of the ATLAS Level-1 topological trigger in Run 2
• Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
• Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics
• Muon trigger with fast Neural Networks on FPGA, a demonstrator
• Accelerating Deep Neural Networks for Real-time Data Selection for High-resolution Imaging Particle Detectors
• Development of a resource-efficient FPGA-based neural network regression model for the ATLAS muon trigger upgrades

Cited by in corpus (5)

• Nanosecond anomaly detection with decision trees and real-time application to exotic Higgs decays
• Ultrafast jet classification on FPGAs for the HL-LHC
• BitHEP -- The Limits of Low-Precision ML in HEP
• Nanosecond hardware regression trees in FPGA at the LHC
• Illuminating all-hadronic final states with a photon: Exotic decays of the Higgs boson to four bottom quarks in vector boson fusion plus gamma at hadron colliders