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, 5
doi: 10.3389/fdata.2022.787421
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | 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. |
| Art des Eintrags: | Bibliographie |
| Titel: | Applications and Techniques for Fast Machine Learning in Science |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Verlag: | Frontiers Media S.A. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Big Data |
| Jahrgang/Volume einer Zeitschrift: | 5 |
| Kollation: | 56 Seiten |
| DOI: | 10.3389/fdata.2022.787421 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | machine learning for science, big data, particle physics, codesign, coprocessors, heterogeneous computing, fast machine learning |
| Sachgruppe der Dewey Dezimalklassifikatin (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 und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Eingebettete Systeme und ihre Anwendungen |
| Hinterlegungsdatum: | 02 Aug 2024 12:40 |
| Letzte Änderung: | 02 Aug 2024 12:40 |
| PPN: | |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Applications and Techniques for Fast Machine Learning in Science. (deposited 09 Mai 2022 13:24)
- Applications and Techniques for Fast Machine Learning in Science. (deposited 02 Aug 2024 12:40) [Gegenwärtig angezeigt]
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