mlsanityData to 7 Oct 2026

Graph Neural Networks for Charged Particle Tracking on FPGAs

arXiv 2112.02048 ↗First posted 3 Dec 2021physics.ins-det cs.AR cs.LGcited by 17

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Abstract (arXiv)

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph -- nodes represent hits, while edges represent possible track segments -- and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called texttthls4ml, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 23 of its 72 references that are in the map. First posted on arXiv 3 Dec 2021.

Builds on

23 of 72 references.

References · 23

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  3. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  4. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  5. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 132
  6. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 78
  7. 2020HyGCN: A GCN Accelerator with Hybrid Architecturecited by 73
  8. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
  9. 2019Learning representations of irregular particle-detector geometry with distance-weighted graph networkscited by 43
  10. 2020Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4MLcited by 42
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  12. 2020GRIP: A Graph Neural Network Accelerator Architecturecited by 37
11 more
  1. 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
  2. 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
  3. 2021Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferencecited by 31
  4. 2019GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platformscited by 29
  5. 2021Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Trackingcited by 29
  6. 2020Fast inference of Boosted Decision Trees in FPGAs for particle physicscited by 26
  7. 2020Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image datacited by 23
  8. 2020Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAscited by 23
  9. 2021Charged particle tracking via edge-classifying interaction networkscited by 19
  10. 2019Graph Processing on FPGAs: Taxonomy, Survey, Challengescited by 6
  11. 2019A Survey on Graph Processing Accelerators: Challenges and Opportunitiescited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 5; 2023: 3; 2024: 5; 2025: 3; 2026: 1.

17 papers.

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  1. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  2. 2022LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physicscited by 12
  3. 2022FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inferencecited by 10
  4. 2022Open-source FPGA-ML codesign for the MLPerf Tiny Benchmarkcited by 9
  5. 2025hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardwarecited by 8
  6. 2024Ultra Fast Transformers on FPGAs for Particle Physics Experimentscited by 6
  7. 2023Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Taskscited by 5
  8. 2024Low Latency Transformer Inference on FPGAs for Physics Applications with hls4mlcited by 5
  9. 2024TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Eracited by 4
  10. 2022Physics Community Needs, Tools, and Resources for Machine Learningcited by 2
  11. 2023GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimizationcited by 1
  12. 2023Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAscited by 1
5 more
  1. 2024Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniquescited by 1
  2. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  3. 2025Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged Particle Reconstructioncited by 1
  4. 2024Exploring End-to-end Differentiable Neural Charged Particle Tracking -- A Loss Landscape Perspectivecited by 0
  5. 2026HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstructioncited by 0