@article{Summers:2026:2632-2153/ae484b,
author = {Summers, S and Tapper, A and Årrestad, TK and Qin, C and Rathsman, K and Streeter, M and Palmer, C and Citrin, J and Zheng, C and Zilberman, N and Titterton, A and Becker, T},
doi = {2632-2153/ae484b},
journal = {Machine Learning: Science and Technology},
pages = {021501--021501},
title = {Roadmap on fast machine learning for science},
url = {http://dx.doi.org/10.1088/2632-2153/ae484b},
volume = {7},
year = {2026}
}

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TY  - JOUR
AB  - <jats:title>Abstract</jats:title>                  <jats:p>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.</jats:p>
AU  - Summers,S
AU  - Tapper,A
AU  - Årrestad,TK
AU  - Qin,C
AU  - Rathsman,K
AU  - Streeter,M
AU  - Palmer,C
AU  - Citrin,J
AU  - Zheng,C
AU  - Zilberman,N
AU  - Titterton,A
AU  - Becker,T
DO  - 2632-2153/ae484b
EP  - 021501
PY  - 2026///
SP  - 021501
TI  - Roadmap on fast machine learning for science
T2  - Machine Learning: Science and Technology
UR  - http://dx.doi.org/10.1088/2632-2153/ae484b
UR  - https://doi.org/10.1088/2632-2153/ae484b
VL  - 7
ER  - 

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