Journal article
Applications and Techniques for Fast Machine Learning in Science
Frontiers in big data, v 5, pp 787421-787421
12 Apr 2022
PMID: 35496379
Abstract
In this community review report, we discuss applications and techniques for
fast
machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.
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Details
- Title
- Applications and Techniques for Fast Machine Learning in Science
- Creators
- Allison McCarn Deiana - Southern Methodist UniversityNhan Tran - Fermi National Accelerator LaboratoryJoshua Agar - Lehigh UniversityMichaela Blott - XilinxGiuseppe Di Guglielmo - Columbia UniversityJavier Duarte - University of California, San DiegoPhilip Harris - Massachusetts Institute of TechnologyScott Hauck - University of WashingtonMia Liu - Purdue University West LafayetteMark S. Neubauer - University of Illinois Urbana-ChampaignJennifer Ngadiuba - Fermi National Accelerator LaboratorySeda Ogrenci-Memik - Northwestern UniversityMaurizio Pierini - European Organization for Nuclear ResearchThea Aarrestad - European Organization for Nuclear ResearchSteffen Bähr - Karlsruhe Institute of TechnologyJürgen Becker - Karlsruhe Institute of TechnologyAnne-Sophie Berthold - , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,Richard J. Bonventre - Lawrence Berkeley National LaboratoryTomás E. Müller Bravo - , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,Markus Diefenthaler - Thomas Jefferson National Accelerator FacilityZhen Dong - University of California, BerkeleyNick Fritzsche - TU DresdenAmir Gholami - University of California, BerkeleyEkaterina Govorkova - European Organization for Nuclear ResearchDongning Guo - Northwestern UniversityKyle J. Hazelwood - Fermi National Accelerator LaboratoryChristian Herwig - , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,Babar Khan - TU DarmstadtSehoon Kim - University of California, BerkeleyThomas Klijnsma - Fermi National Accelerator LaboratoryYaling Liu - Lehigh UniversityKin Ho Lo - University of FloridaTri Nguyen - Massachusetts Institute of TechnologyGianantonio Pezzullo - Yale UniversitySeyedramin Rasoulinezhad - University of SydneyRyan A. Rivera - Fermi National Accelerator LaboratoryKate Scholberg - Duke UniversityJustin Selig - , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,Sougata Sen - Birla Institute of Technology and Science, PilaniDmitri Strukov - University of California, Santa BarbaraWilliam Tang - Princeton UniversitySavannah Thais - Princeton UniversityKai Lukas Unger - Karlsruhe Institute of TechnologyRicardo Vilalta - University of HoustonBelina von Krosigk - Karlsruhe Institute of TechnologyShen Wang - Lehigh UniversityThomas K. Warburton - Iowa State UniversityFermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
- Publication Details
- Frontiers in big data, v 5, pp 787421-787421
- Publisher
- Frontiers Media S.A
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mechanical Engineering and Mechanics
- Web of Science ID
- WOS:000808004900001
- Scopus ID
- 2-s2.0-85128972935
- Other Identifier
- 991021889909604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Interdisciplinary Applications