| Article | |
| Title | Roadmap on fast machine learning for science |
| Author(s) | Summers, Sioni (CERN) ; Tapper, Alex (Imperial Coll., London) ; Årrestad, Thea Klæboe (ETH, Zurich (main)) ; Qin, Chen (Imperial Coll., London) ; Rathsman, Karin (ESS, Lund) ; Streeter, Matthew (Queen's U., Belfast) ; Palmer, Charlotte (Queen's U., Belfast) ; Citrin, Jonathan (Unlisted, UK) ; Zheng, Changgang (U. Oxford (main)) ; Zilberman, Noa (U. Oxford (main)) ; Titterton, Alexander (Unlisted, UK) ; Becker, Tobias (Unlisted, UK) |
| Publication | 2026 |
| Number of pages | 30 |
| In: | Mach. Learn. Sci. Tech. 7 (2026) 021501 |
| DOI | 10.1088/2632-2153/ae484b |
| Subject category | Data Analysis and Statistics ; Computing and Computers |
| Abstract | The need for microsecond speed machine learning (ML) inference for particle physics experiments has emerged in recent years, in particular for the forthcoming upgrades to the experiments at the Large Hadron Collider at CERN. A community has grown around the need to develop the custom hardware platforms and tools required. The material presented in this report is drawn from the latest workshop held by the fast ML for science community and comprises of a collection of perspectives on the status of fast ML in different scientific domains, and the supporting technology. |
| Copyright/License | publication: © 2026 The Author(s) (License: CC-BY-4.0) |
Corresponding record in: Inspire
Datensatz erzeugt am 2026-04-01, letzte Änderung am 2026-04-01
