mlsanityData to 7 Oct 2026

Fast inference of Boosted Decision Trees in FPGAs for particle physics

arXiv 2002.02534 ↗First posted 5 Feb 2020physics.comp-ph astro-ph.IM cs.LGcited by 26

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Abstract (arXiv)

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 developments open up prospects for physicists to deploy BDTs in FPGAs for identifying the origin of jets, better reconstructing the energies of muons, and enabling better selection of rare signal processes.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 5; 2023: 2; 2024: 5; 2025: 5; 2026: 1.

26 papers.

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  1. 2021A Living Review of Machine Learning for Particle Physicscited by 77
  2. 2021hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devicescited by 61
  3. 2021Machine Learning in the Search for New Fundamental Physicscited by 50
  4. 2021Fast convolutional neural networks on FPGAs with hls4mlcited by 42
  5. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
  6. 2023PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inferencecited by 30
  7. 2024NeuraLUT: Hiding Neural Network Density in Boolean Synthesizable Functionscited by 21
  8. 2021Charged particle tracking via edge-classifying interaction networkscited by 19
  9. 2021Graph Neural Networks for Charged Particle Tracking on FPGAscited by 17
  10. 2025TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Treescited by 15
  11. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  12. 2024Ultrafast jet classification on FPGAs for the HL-LHCcited by 12
14 more
  1. 2022Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4mlcited by 11
  2. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  3. 2021Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physicscited by 9
  4. 2022Open-source FPGA-ML codesign for the MLPerf Tiny Benchmarkcited by 9
  5. 2025PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruningcited by 8
  6. 2024rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGAcited by 3
  7. 2022Physics Community Needs, Tools, and Resources for Machine Learningcited by 2
  8. 2023Fast Inference of Tree Ensembles on ARM Devicescited by 1
  9. 2024Embedded FPGA Developments in 130nm and 28nm CMOS for Machine Learning in Particle Detector Readoutcited by 1
  10. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  11. 2024Investigating Resource-efficient Neutron/Gamma Classification ML Models Targeting eFPGAscited by 0
  12. 2025Neural Network Acceleration on MPSoC board: Integrating SLAC's SNL, Rogue Software and Auto-SNLcited by 0
  13. 2025SparsePixels: Efficient Convolution for Sparse Data on FPGAscited by 0
  14. 2026FQTree: Fine-grained Quantization and Hardware Generation of Boosted Decision Treescited by 0