# Fast inference of Boosted Decision Trees in FPGAs for particle physics

arXiv: https://arxiv.org/abs/2002.02534 · HTML: https://mlsanity.com/paper/2002.02534/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 26 of 26)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices](https://mlsanity.com/paper/2103.05579/) (2021)
- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
- [Fast convolutional neural networks on FPGAs with hls4ml](https://mlsanity.com/paper/2101.05108/) (2021)
- [Applications and Techniques for Fast Machine Learning in Science](https://mlsanity.com/paper/2110.13041/) (2021)
- [PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference](https://mlsanity.com/paper/2309.02334/) (2023)
- [NeuraLUT: Hiding Neural Network Density in Boolean Synthesizable Functions](https://mlsanity.com/paper/2403.00849/) (2024)
- [Charged particle tracking via edge-classifying interaction networks](https://mlsanity.com/paper/2103.16701/) (2021)
- [Graph Neural Networks for Charged Particle Tracking on FPGAs](https://mlsanity.com/paper/2112.02048/) (2021)
- [TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Trees](https://mlsanity.com/paper/2501.01511/) (2025)
- [Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges](https://mlsanity.com/paper/2203.12852/) (2022)
- [Ultrafast jet classification on FPGAs for the HL-LHC](https://mlsanity.com/paper/2402.01876/) (2024)
- [Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml](https://mlsanity.com/paper/2205.07690/) (2022)
- [Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning](https://mlsanity.com/paper/2209.07559/) (2022)
- [Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics](https://mlsanity.com/paper/2104.03408/) (2021)
- [Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark](https://mlsanity.com/paper/2206.11791/) (2022)
- [PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning](https://mlsanity.com/paper/2501.08043/) (2025)
- [rule4ml: An Open-Source Tool for Resource Utilization and Latency Estimation for ML Models on FPGA](https://mlsanity.com/paper/2408.05314/) (2024)
- [Physics Community Needs, Tools, and Resources for Machine Learning](https://mlsanity.com/paper/2203.16255/) (2022)
- [Fast Inference of Tree Ensembles on ARM Devices](https://mlsanity.com/paper/2305.08579/) (2023)
- [Embedded FPGA Developments in 130nm and 28nm CMOS for Machine Learning in Particle Detector Readout](https://mlsanity.com/paper/2404.17701/) (2024)
- [BitHEP -- The Limits of Low-Precision ML in HEP](https://mlsanity.com/paper/2504.03387/) (2025)
- [Investigating Resource-efficient Neutron/Gamma Classification ML Models Targeting eFPGAs](https://mlsanity.com/paper/2404.14436/) (2024)
- [Neural Network Acceleration on MPSoC board: Integrating SLAC's SNL, Rogue Software and Auto-SNL](https://mlsanity.com/paper/2508.21739/) (2025)
- [SparsePixels: Efficient Convolution for Sparse Data on FPGAs](https://mlsanity.com/paper/2512.06208/) (2025)
- [FQTree: Fine-grained Quantization and Hardware Generation of Boosted Decision Trees](https://mlsanity.com/paper/2608.12140/) (2026)

## 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.