Applications and Techniques for Fast Machine Learning in Science
Creators: name: Deiana, Allison McCarn
name: Tran, Nhan
name: Agar, Joshua
name: Blott, Michaela
name: Di Guglielmo, Giuseppe
name: Duarte, Javier
name: Harris, Philip
name: Hauck, Scott
name: Liu, Mia
name: Neubauer, Mark S.
name: Ngadiuba, Jennifer
name: Ogrenci-Memik, Seda
name: Pierini, Maurizio
name: Aarrestad, Thea
name: Bahr, Steffen
name: Becker, Jurgen
name: Berthold, Anne-Sophie
name: Bonventre, Richard J.
name: Bravo, Tomas E. Muller
name: Diefenthaler, Markus
name: Dong, Zhen
name: Fritzsche, Nick
name: Gholami, Amir
name: Govorkova, Ekaterina
name: Hazelwood, Kyle J
name: Herwig, Christian
name: Khan, Babar
name: Kim, Sehoon
name: Klijnsma, Thomas
name: Liu, Yaling
name: Lo, Kin Ho
name: Nguyen, Tri
name: Pezzullo, Gianantonio
name: Rasoulinezhad, Seyedramin
name: Rivera, Ryan A.
name: Scholberg, Kate
name: Selig, Justin
name: Sen, Sougata
name: Strukov, Dmitri
name: Tang, William
name: Thais, Savannah
name: Unger, Kai Lukas
name: Vilalta, Ricardo
name: Krosigk, Belinavon
name: Warburton, Thomas K.
name: Flechas, Maria Acosta
name: Aportela, Anthony
name: Calvet, Thomas
name: Cristella, Leonardo
name: Diaz, Daniel
name: Doglioni, Caterina
name: Galati, Maria Domenica
name: Khoda, Elham E
name: Fahim, Farah
name: Giri, Davide
name: Hawks, Benjamin
name: Hoang, Duc
name: Holzman, Burt
name: Hsu, Shih-Chieh
name: Jindariani, Sergo
name: Johnson, Iris
name: Kansal, Raghav
name: Kastner, Ryan
name: Katsavounidis, Erik
name: Krupa, Jeffrey
name: Li, Pan
name: Madireddy, Sandeep
name: Marx, Ethan
name: McCormack, Patrick
name: Meza, Andres
name: Mitrevski, Jovan
name: Mohammed, Mohammed Attia
name: Mokhtar, Farouk
name: Moreno, Eric
name: Nagu, Srishti
name: Narayan, Rohin
name: Palladino, Noah
name: Que, Zhiqiang
name: Park, Sang Eon
name: Ramamoorthy, Subramanian
name: Rankin, Dylan
name: Rothman, Simon
name: Sharma, Ashish
name: Summers, Sioni
name: Vischia, Pietro
name: Vlimant, Jean-Roch
name: Weng, Olivia
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (cs.LG)
subject: Hardware Architecture (cs.AR)
subject: Data Analysis, Statistics and Probability (physics.data-an)
subject: Instrumentation and Detectors (physics.ins-det)
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: FOS: Physical sciences
subject: FOS: Physical sciences

description: 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.
description: 66 pages, 13 figures, 5 tables