Open Access
| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01042 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/epjconf/202533701042 | |
| Published online | 07 October 2025 | |
- I. Zurbano Fernandez et al., CERN-2020-010 (2020). 10.23731/CYRM-2020-0010 [Google Scholar]
- ATLAS Collaboration, CERN-LHCC-2015-020 (2015). [Google Scholar]
- ATLAS Collaboration, CERN-LHCC-2017-021 (2017). [Google Scholar]
- ATLAS Collaboration, CERN-LHCC-2017-005 (2017). [Google Scholar]
- ATLAS Collaboration, CERN-LHCC-2017-020 (2017). [Google Scholar]
- ATLAS Collaboration, ATLAS Inner Tracker Layout 03-00-00 (2023), https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PLOTS/ITK-2023-001/ [Google Scholar]
- ATLAS Collaboration, CERN-LHCC-2022-004 (2022). [Google Scholar]
- S. Dittmeier (ATLAS TDAQ), Track reconstruction for the ATLAS Phase-II Event Filter using GNNs on FPGAs, EPJ Web Conf. 295, 02032 (2024). 10.1051/epjconf/202429502032 [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
- S. Caillou et al. (ATLAS), ATLAS ITk Track Reconstruction with a GNN-based pipeline (2022), https://cds.cern.ch/record/2815578 [Google Scholar]
- M.J. Atkinson et al., ACORN, https://github.com/GNN4ITkTeam/CommonFramework [Google Scholar]
- ATLAS Collaboration, Computational Performance of the ATLAS ITk GNN Track Reconstruction Pipeline, ATL-PHYS-PUB-2024-018 (2024). [Google Scholar]
- P.W. Battaglia et al., Interaction Networks for Learning about Objects, Relations and Physics (2016), 1612.00222. [Google Scholar]
- AMD, Vitis unified software platform documentation: Embedded software development (ug1400) (2023), https://docs.xilinx.com/r/en-US/ug1400-vitis-embedded [Google Scholar]
- A. Paszke et al., Pytorch: An imperative style, high-performance deep learning library, Advances in neural information processing systems 32 (2019). [Google Scholar]
- A. Pappalardo, Xilinx/brevitas (2023), https://doi.org/10.5281/zenodo. 3333552 [Google Scholar]
- A. Pappalardo et al., QONNX: Representing Arbitrary-Precision Quantized Neural Networks, in 4th AccML at HiPEAC 2022 Conference (2022), 2206.07527 [Google Scholar]
- M. Blott et al., Finn-r: An end-to-end deep-learning framework for fast exploration of quantized neural networks, ACM TRETS 11, 1 (2018). [Google Scholar]
- ATLAS Collaboration, Approved plots for the EF Tracking project (2024), https://twiki.cern.ch/twiki/bin/view/AtlasPublic/EFTrackingPublicResults [Google Scholar]
- L. He et al., The connected-component labeling problem: A review of state-of-the-art algorithms, Pattern Recognition 70, 25 (2017). https://doi.org/10.1016/j.patcog.2017.04.018 [Google Scholar]
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