Research
Papers
- F. Wojcicki et al., Accelerating Transformer Neural Networks on FPGAs for High Energy Physics Experiments, FPT 2022 Conference Proceedings.
- M. Barbone et al., GPU acceleration of Monte Carlo simulations: particle physics methods applied to medicine, ACAT 2022 Conference Proceedings.
- L. Borgna et al., Accelerating the DBSCAN clustering algorithm for low-latency primary vertex reconstruction, ACAT 2022 Conference Proceedings.
- Z. Que et al., LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors, under review.
- Z. Que et al., Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs, FPL 2022 Conference Proceedings.
- L. Våge, Accelerated graph building for particle tracking graph neural nets, CTD 2022 Conference Proceedings.
- C. Brown et al., Track Finding and Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System, CTD 2022 Conference Proceedings.
- C. Brown et al., Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System, ACAT 2021 Conference Proceedings.
- M. Barbone et al., Demonstration of FPGA Acceleration of Monte Carlo Simulation, ACAT 2021 Conference Proceedings.
Talks & posters
- C. Brown, The Deployment of Realtime ML in Changing Environments CHEP 2023.
- M. Barbone, Fast, high-quality pseudo random number generators for heterogeneous computing CHEP 2023.
- M. Barbone, Embedded Continual Learning for HEP CHEP 2023.
- M. Barbone, Acceleration of a CMS DNN based Algorithm CHEP 2023.
- M. Mieskolainen, HyperTrack: neural combinatorics for high energy physics CHEP 2023.
- F. Wojcicki et al., Accelerating Transformer Neural Networks on FPGAs for High Energy Physics Experiments, FPT 2022.
- M. Barbone et al., GPU acceleration of Monte Carlo simulations: particle physics methods applied to medicine, ACAT 2022.
- L. Borgna et al., Accelerating the DBSCAN clustering algorithm for low-latency primary vertex reconstruction, ACAT 2022.
- C. Brown, Development of a DNN for Vertexing and Track to vertex association in the GTT, ML@L1 Trigger Workshop at the LPC, 2022.
- C. Brown et al., End-to-End Vertex Finding for the CMS Level-1 Trigger, Fast Machine Learning for Science Workshop 2022.
- Z. Que et al., Accelerating JEDI-net for jet tagging on FPGAs, Fast Machine Learning for Science Workshop 2022.
- B. Radburn-Smith et al., Deployment of ML in changing environments, Fast Machine Learning for Science Workshop 2022.
- Z. Que et al., Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs, Monthly Fast ML meeting.
- Z. Que et al., Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs, FPL 2022.
- L. Våge et al., Accelerated graph building for particle tracking graph neural nets, Connecting The Dots 2022.
- C. Brown et al., Track Finding and Neural Network-Based Primary Vertex Reconstruction with FPGAs for the Upgrade of the CMS Level-1 Trigger System, Connecting The Dots 2022.
- T. Ourida et al., FPGA acceleration of the CMS DNN based LLP Jet Algorithm for the LHC High-Luminosity upgrade, 5th Inter-experiment Machine Learning Workshop, CERN, 2022.
- A. Rose, Centre for High-Throughput Digital Electronics and Embedded Machine Learning, Towards the future of AI, Imperial College London, 2022.
- L. Borgna, Fast Primary Vertex and Track Reconstruction Methods, SwiftHEP Workshop 2022.
- M. Barbone et al., Simulations: A case study, SwiftHep/ExcaliburHep Workshop, 2021.
- L. Våge et al., Accelerating particle tracking for the HL-LHC, SwiftHep/ExcaliburHep Workshop 2021.
- M. Barbone et al., Demonstration of FPGA Acceleration of Monte Carlo Simulation, ACAT 2021.
- C. Brown et al., Neural network based primary vertex reconstruction with FPGAs for the upgrade of the CMS level-1 trigger system, ACAT 2021.
- M. Barbone, Demonstration of FPGA Acceleration of Monte Carlo Simulation, Geant4 simulation collaboration bi-weekly meeting, 2022.
- M. Barbone, FPGA Acceleration of Monte Carlo Simulation, HEP Software Foundation Detector Simulation Working Group, 2021.
Code repositories
- Github Repository for Centre for Embedded Machine-learning and High-throughput Digital Electronics at Imperial College
- GNN-JEDInet-FPGA repository for HLS-based template for the GNN-based JEDI-net
- Repository for Multiple Scattering Monte Carlo code