TY - JOUR
T1 - Applications and Techniques for Fast Machine Learning in Science
AU - Deiana, Allison Mc Carn
AU - Tran, Nhan
AU - Agar, Joshua
AU - Blott, Michaela
AU - Di Guglielmo, Giuseppe
AU - Duarte, Javier
AU - Harris, Philip
AU - Hauck, Scott
AU - Liu, Mia
AU - Neubauer, Mark S.
AU - Ngadiuba, Jennifer
AU - Ogrenci-Memik, Seda
AU - Pierini, Maurizio
AU - Aarrestad, Thea
AU - Bähr, Steffen
AU - Becker, Jürgen
AU - Berthold, Anne Sophie
AU - Bonventre, Richard J.
AU - Müller Bravo, Tomás E.
AU - Diefenthaler, Markus
AU - Dong, Zhen
AU - Fritzsche, Nick
AU - Gholami, Amir
AU - Govorkova, Ekaterina
AU - Guo, Dongning
AU - Hazelwood, Kyle J.
AU - Herwig, Christian
AU - Khan, Babar
AU - Kim, Sehoon
AU - Klijnsma, Thomas
AU - Liu, Yaling
AU - Lo, Kin Ho
AU - Nguyen, Tri
AU - Pezzullo, Gianantonio
AU - Rasoulinezhad, Seyedramin
AU - Rivera, Ryan A.
AU - Scholberg, Kate
AU - Selig, Justin
AU - Sen, Sougata
AU - Strukov, Dmitri
AU - Tang, William
AU - Thais, Savannah
AU - Unger, Kai Lukas
AU - Vilalta, Ricardo
AU - von Krosigk, Belina
AU - Wang, Shen
AU - Warburton, Thomas K.
N1 - Publisher Copyright:
Copyright © 2022 Deiana, Tran, Agar, Blott, Di Guglielmo, Duarte, Harris, Hauck, Liu, Neubauer, Ngadiuba, Ogrenci-Memik, Pierini, Aarrestad, Bähr, Becker, Berthold, Bonventre, Müller Bravo, Diefenthaler, Dong, Fritzsche, Gholami, Govorkova, Guo, Hazelwood, Herwig, Khan, Kim, Klijnsma, Liu, Lo, Nguyen, Pezzullo, Rasoulinezhad, Rivera, Scholberg, Selig, Sen, Strukov, Tang, Thais, Unger, Vilalta, von Krosigk, Wang and Warburton.
PY - 2022/4/12
Y1 - 2022/4/12
N2 - 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.
AB - 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.
KW - big data
KW - codesign
KW - coprocessors
KW - fast machine learning
KW - heterogeneous computing
KW - machine learning for science
KW - particle physics
UR - http://www.scopus.com/inward/record.url?scp=85128972935&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128972935&partnerID=8YFLogxK
U2 - 10.3389/fdata.2022.787421
DO - 10.3389/fdata.2022.787421
M3 - Review article
C2 - 35496379
AN - SCOPUS:85128972935
SN - 2624-909X
VL - 5
JO - Frontiers in Big Data
JF - Frontiers in Big Data
M1 - 787421
ER -