Fast inference of Boosted Decision Trees in FPGAs for particle physics

  title={Fast inference of Boosted Decision Trees in FPGAs for particle physics},
  author={Sioni Summers and Giuseppe Di Guglielmo and Javier Mauricio Duarte and Philip C. Harris and Duc A. Hoang and Sergo Jindariani and Edward Kreinar and Vladimir Loncar and Jennifer Ngadiuba and Maurizio Pierini and Dylan S. Rankin and Nhan Viet Tran and Zhenbin Wu},
  journal={Journal of Instrumentation},
  pages={P05026 - P05026}
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… 

Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics

This work presents a novel implementation of classification using the machine learning/artificial intelligence method called boosted decision trees (BDT) on field programmable gate arrays (FPGA) and aims to provide decisions at the lowest latency values for real-time event classification.

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

A representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider is considered, and a graph network architecture developed for such purposes is used, and additional simplifications to match the computing constraints of Level-1 trigger systems are applied.

Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml

We present the implementation of binary and ternary neural networks in the hls4ml library, designed to automatically convert deep neural network models to digital circuits with field-programmable

Fast convolutional neural networks on FPGAs with hls4ml

An automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on field-programmable gate arrays (FPGAs) is introduced and it is shown that the FPGA critical resource consumption can be reduced by 97% with zero loss in model accuracy, and by 99% when tolerating a 6% accuracy degradation.

Triggering long-lived particles in HL-LHC and the challenges in the first stage of the trigger system

Triggering long-lived particles (LLPs) at the first stage of the trigger system is very crucial in LLP searches to ensure that we do not miss them at the very beginning. The future High Luminosity

Fast Inference for Machine Learning in ROOT/TMVA

The new developments and strategy of TMVA are presented, which will allow the analyst to integrate seamlessly, and effectively, different workflows in the diversified machine-learning landscape.

HL-LHC Computing Review: Common Tools and Community Software

This document addresses the issues for software that is used in multiple experiments and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity.

A Living Review of Machine Learning for Particle Physics

This living review is a nearly comprehensive list of citations for those developing and applying deep learning approaches to experimental, phenomenological, or theoretical analyses, and will be updated as often as possible to incorporate the latest developments.

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices

Hls4ml, an open-source software-hardware co-design workflow to interpret and translate machine learning algorithms for implementation in FPGAs and ASICs specifically to support domain scientists, is developed.

Fast muon tracking with machine learning implemented in FPGA



Fast inference of deep neural networks in FPGAs for particle physics

A case study for neural network inference in FPGAs focusing on a classifier for jet substructure which would enable, among many other physics scenarios, searches for new dark sector particles and novel measurements of the Higgs boson.

Boosted Decision Trees in the Level-1 Muon Endcap Trigger at CMS

The first implementation of a Machine Learning Algorithm inside a Level-1 trigger system at the LHC is presented and the new momentum algorithm reduced the background rate by a factor of three with respect to the previous analytic algorithm.

Efficient, reliable and fast high-level triggering using a bonsai boosted decision tree

A modification to the standard boosted decision tree (BDT) classifier, the so-called bonsai BDT, that is more efficient than traditional cut-based approaches; it is robust against detector instabilities, and it is very fast.

Distributed Inference over Decision Tree Ensembles on Clusters of FPGAs

This article explores the efficient construction of FPGA clusters using inference over Decision Tree Ensembles as the target application and shows that the resulting system can support inference over decision tree ensembles at a significantly higher throughput than that achieved by existing systems.

FPGA Implementation of Decision Trees and Tree Ensembles for Character Recognition in Vivado Hls

An FPGA implementation of decision trees and tree ensembles for letter and digit recognition in Vivado High-Level Synthesis is presented and classification accuracy, throughput and resource usage for different training algorithms, tree depths and ensemble sizes are discussed.

Scalable inference of decision tree ensembles: Flexible design for CPU-FPGA platforms

This paper presents an FPGA tree ensemble classifier together with a software driver to efficiently manage theFPGA's memory resources, delivering up to 20× speedup over a 10-threaded CPU implementation when fully processing the tree ensemble on the FPGAs.

Deep Learning and Its Application to LHC Physics

The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

Decision Tree and Random Forest Implementations for Fast Filtering of Sensor Data

This paper investigates architectural characteristics of embedded systems for filtering high-volume sensor data before further processing and investigates implementations of decision trees and random forests for the classical von-Neumann computing architecture and custom circuits by the means of field programmable gate arrays.