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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. (Publisher's Version)
In: Frontiers in Big Data, 5, Frontiers Media S.A., e-ISSN 2624-909X,
DOI: 10.26083/tuprints-00021245,
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Item Type: Article
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: Applications and Techniques for Fast Machine Learning in Science
Language: English
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.

Journal or Publication Title: Frontiers in Big Data
Volume of the journal: 5
Place of Publication: Darmstadt
Publisher: Frontiers Media S.A.
Collation: 56 Seiten
Uncontrolled Keywords: machine learning for science, big data, particle physics, codesign, coprocessors, heterogeneous computing, fast machine learning
Classification DDC: 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
500 Naturwissenschaften und Mathematik > 530 Physik
600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 20 Department of Computer Science > Embedded Systems and Applications
Date Deposited: 09 May 2022 13:24
Last Modified: 29 Sep 2022 06:25
DOI: 10.26083/tuprints-00021245
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-212450
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21245
PPN: 499758870
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