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Applications and Techniques for Fast Machine Learning in Science

McCarn Deiana, Allison ; Tran, Nhan ; Agar, Joshua ; Blott, Michaela ; Di Guglielmo, Giuseppe ; Duarte, Javier ; Harris, Philip ; Hauck, Scott ; Liu, Mia ; Neubauer, Mark S. ; Ngadiuba, Jennifer ; Ogrenci-Memik, Seda ; Pierini, Maurizio ; Aarrestad, Thea ; Bähr, Steffen ; Becker, Jürgen ; Berthold, Anne-Sophie ; Bonventre, Richard J. ; Müller Bravo, Tomás E. ; Diefenthaler, Markus ; Dong, Zhen ; Fritzsche, Nick ; Gholami, Amir ; Govorkova, Ekaterina ; Guo, Dongning ; Hazelwood, Kyle J. ; Herwig, Christian ; Khan, Babar ; Kim, Sehoon ; Klijnsma, Thomas ; Liu, Yaling ; Lo, Kin Ho ; Nguyen, Tri ; Pezzullo, Gianantonio ; Rasoulinezhad, Seyedramin ; Rivera, Ryan A. ; Scholberg, Kate ; Selig, Justin ; Sen, Sougata ; Strukov, Dmitri ; Tang, William ; Thais, Savannah ; Unger, Kai Lukas ; Vilalta, Ricardo ; Krosigk, Belina von ; Wang, Shen ; Warburton, Thomas K. (2022)
Applications and Techniques for Fast Machine Learning in Science.
In: Frontiers in Big Data, 2022, 5
doi: 10.26083/tuprints-00021245
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Applications and Techniques for Fast Machine Learning in Science
Language: English
Date: 9 May 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: Frontiers Media S.A.
Journal or Publication Title: Frontiers in Big Data
Volume of the journal: 5
Collation: 56 Seiten
DOI: 10.26083/tuprints-00021245
Corresponding Links:
Origin: Secondary publication DeepGreen

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.

Uncontrolled Keywords: machine learning for science, big data, particle physics, codesign, coprocessors, heterogeneous computing, fast machine learning
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-212450
Classification DDC: 000 Generalities, computers, information > 004 Computer science
500 Science and mathematics > 530 Physics
600 Technology, medicine, applied sciences > 600 Technology
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 20 Department of Computer Science > Embedded Systems and Applications
Date Deposited: 09 May 2022 13:24
Last Modified: 14 Nov 2023 19:04
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21245
PPN: 499758870
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