mlsanityData to 14 Oct 2026

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

arXiv 2012.01563 ↗First posted 30 Nov 2020physics.ins-det cs.LG hep-excited by 23

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Abstract (arXiv)

We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2020.

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

23 papers.

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  1. 2021A Living Review of Machine Learning for Particle Physicscited by 78
  2. 2021Fast convolutional neural networks on FPGAs with hls4mlcited by 42
  3. 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
  4. 2021Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Trackingcited by 30
  5. 2021Charged particle tracking via edge-classifying interaction networkscited by 19
  6. 2021Graph Neural Networks for Charged Particle Tracking on FPGAscited by 17
  7. 2021Online-compatible Unsupervised Non-resonant Anomaly Detectioncited by 15
  8. 2021The Tracking Machine Learning challenge : Throughput phasecited by 12
  9. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  10. 2022Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4mlcited by 11
  11. 2022Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learningcited by 10
  12. 2021Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physicscited by 9
11 more
  1. 2021Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezingcited by 7
  2. 2022Explainable AI for High Energy Physicscited by 7
  3. 2022A Detailed Study of Interpretability of Deep Neural Network based Top Taggerscited by 7
  4. 2021Shared Data and Algorithms for Deep Learning in Fundamental Physicscited by 4
  5. 2024TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Eracited by 4
  6. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 3
  7. 2025Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networkscited by 3
  8. 2023Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAscited by 1
  9. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  10. 2024Exploring End-to-end Differentiable Neural Charged Particle Tracking -- A Loss Landscape Perspectivecited by 0
  11. 2025Geometric GNNs for Charged Particle Tracking at GlueXcited by 0