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Applications and Techniques for Fast Machine Learning in Science

Authors :
Deiana, Allison McCarn
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
Bahr, Steffen
Becker, Jurgen
Berthold, Anne-Sophie
Bonventre, Richard J.
Bravo, Tomas E. Muller
Diefenthaler, Markus
Dong, Zhen
Fritzsche, Nick
Gholami, Amir
Govorkova, Ekaterina
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, Belinavon
Warburton, Thomas K.
Flechas, Maria Acosta
Aportela, Anthony
Calvet, Thomas
Cristella, Leonardo
Diaz, Daniel
Doglioni, Caterina
Galati, Maria Domenica
Khoda, Elham E
Fahim, Farah
Giri, Davide
Hawks, Benjamin
Hoang, Duc
Holzman, Burt
Hsu, Shih-Chieh
Jindariani, Sergo
Johnson, Iris
Kansal, Raghav
Kastner, Ryan
Katsavounidis, Erik
Krupa, Jeffrey
Li, Pan
Madireddy, Sandeep
Marx, Ethan
McCormack, Patrick
Meza, Andres
Mitrevski, Jovan
Mohammed, Mohammed Attia
Mokhtar, Farouk
Moreno, Eric
Nagu, Srishti
Narayan, Rohin
Palladino, Noah
Que, Zhiqiang
Park, Sang Eon
Ramamoorthy, Subramanian
Rankin, Dylan
Rothman, Simon
Sharma, Ashish
Summers, Sioni
Vischia, Pietro
Vlimant, Jean-Roch
Weng, Olivia
Source :
Front. Big Data 5, 787421 (2022)
Publication Year :
2021

Abstract

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power 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.<br />Comment: 66 pages, 13 figures, 5 tables

Details

Database :
arXiv
Journal :
Front. Big Data 5, 787421 (2022)
Publication Type :
Report
Accession number :
edsarx.2110.13041
Document Type :
Working Paper
Full Text :
https://doi.org/10.3389/fdata.2022.787421