mlsanityData to 14 Oct 2026

Ultrafast jet classification on FPGAs for the HL-LHC

arXiv 2402.01876 ↗First posted 2 Feb 2024hep-ex cs.LG physics.ins-detcited by 12

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Abstract (arXiv)

Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN LHC during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that O(100) ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 27 of its 54 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

27 of 54 references.

References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  3. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  4. 2018The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkscited by 2,262
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  6. 2017Deep Setscited by 1,421
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  8. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
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  11. 2020What is the State of Neural Network Pruning?cited by 583
  12. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 509
15 more
  1. 2020Comparing Rewinding and Fine-tuning in Neural Network Pruningcited by 240
  2. 2019Deconstructing Lottery Tickets: Zeros, Signs, and the Supermaskcited by 207
  3. 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 133
  4. 2022Particle Transformer for Jet Taggingcited by 98
  5. 2018Energy Flow Networks: Deep Sets for Particle Jetscited by 84
  6. 2020Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectorscited by 80
  7. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
  8. 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
  9. 2018Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directionscited by 27
  10. 2020Fast inference of Boosted Decision Trees in FPGAs for particle physicscited by 26
  11. 2022PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physicscited by 26
  12. 2023Explainable Equivariant Neural Networks for Particle Physics: PELICANcited by 24
  13. 2022LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physicscited by 12
  14. 2021Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physicscited by 9
  15. 2023Fast Neural Network Inference on FPGAs for Triggering on Long-Lived Particles at Colliderscited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 4.

12 papers.

  1. 2024HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAscited by 9
  2. 2025hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardwarecited by 9
  3. 2025da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAscited by 8
  4. 2025Sub-microsecond Transformers for Jet Tagging on FPGAscited by 6
  5. 2024Ultra-low latency quantum-inspired machine learning predictors implemented on FPGAcited by 4
  6. 2025MetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep Learning Accelerationcited by 4
  7. 2024Jet Discrimination with Quantum Complete Graph Neural Networkcited by 3
  8. 2026JetFormer: A Scalable and Efficient Transformer for Jet Tagging from Offline Analysis to FPGA Triggerscited by 3
  9. 2026Towards Tensor Network Models for Low-Latency Jet Tagging on FPGAscited by 2
  10. 2026HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inferencecited by 1
  11. 2025Knowledge is Overrated: A zero-knowledge machine learning and cryptographic hashing-based framework for verifiable, low latency inference at the LHCcited by 0
  12. 2026SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesigncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Fast Jet Tagging with MLP-Mixers on FPGAs
  2. 2022Lightweight Jet Reconstruction and Identification as an Object Detection Task
  3. 2025JEDI-linear: Fast and Efficient Graph Neural Networks for Jet Tagging on FPGAs
  4. 2021Fast convolutional neural networks on FPGAs with hls4ml
  5. 2023End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs
  6. 2026Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml
  7. 2023Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
  8. 2025Multimodal Generative Flows for LHC Jets