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 |
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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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