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

arXiv 2008.03601 ↗First posted 8 Aug 2020physics.ins-det cs.LG hep-excited by 34

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Abstract (arXiv)

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than 1μs on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the mathtthls4ml library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Aug 2020.

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Citing papers per year: 2021: 11; 2022: 12; 2023: 2; 2024: 3; 2025: 5; 2026: 1.

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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. 2021Fast convolutional neural networks on FPGAs with hls4mlcited by 42
  4. 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
  5. 2021Applications and Techniques for Fast Machine Learning in Sciencecited by 32
  6. 2024Towards Effective and General Graph Unlearning via Mutual Evolutioncited by 29
  7. 2022PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physicscited by 26
  8. 2021Charged particle tracking via edge-classifying interaction networkscited by 19
  9. 2021Graph Neural Networks for Charged Particle Tracking on FPGAscited by 17
  10. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  11. 2022LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physicscited by 12
  12. 2024Ultrafast jet classification on FPGAs for the HL-LHCcited by 12
22 more
  1. 2022Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4mlcited by 11
  2. 2021Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distancecited by 10
  3. 2022FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inferencecited by 10
  4. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  5. 2021Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physicscited by 9
  6. 2022GenGNN: A Generic FPGA Framework for Graph Neural Network Accelerationcited by 9
  7. 2022Open-source FPGA-ML codesign for the MLPerf Tiny Benchmarkcited by 9
  8. 2025hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardwarecited by 8
  9. 2022Explainable AI for High Energy Physicscited by 7
  10. 2023FPGA Resource-aware Structured Pruning for Real-Time Neural Networkscited by 7
  11. 2022End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networkscited by 6
  12. 2022A Detailed Study of Interpretability of Deep Neural Network based Top Taggerscited by 6
  13. 2021Learning Graph Representationscited by 5
  14. 2025Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networkscited by 3
  15. 2022Physics Community Needs, Tools, and Resources for Machine Learningcited by 2
  16. 2023Graph Structure from Point Clouds: Geometric Attention is All You Needcited by 2
  17. 2026Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAscited by 2
  18. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  19. 2021Deep Neural Network as an alternative to Boosted Decision Trees for PIDcited by 0
  20. 2024HPCNeuroNet: A Neuromorphic Approach Merging SNN Temporal Dynamics with Transformer Attention for FPGA-based Particle Physicscited by 0
  21. 2025Learning Before Filtering: Real-Time Hardware Learning at the Detector Levelcited by 0
  22. 2025Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systemscited by 0