Fast inference of Boosted Decision Trees in FPGAs for particle physics

@article{Summers2020FastIO,
  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},
  year={2020},
  volume={15},
  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… 

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References

SHOWING 1-10 OF 21 REFERENCES

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.