Applications and Techniques for Fast Machine Learning in Science
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.
Cite
@article{arxiv.2110.13041,
title = {Applications and Techniques for Fast Machine Learning in Science},
author = {Allison McCarn Deiana and Nhan Tran and Joshua Agar and Michaela Blott and Giuseppe Di Guglielmo and Javier Duarte and Philip Harris and Scott Hauck and Mia Liu and Mark S. Neubauer and Jennifer Ngadiuba and Seda Ogrenci-Memik and Maurizio Pierini and Thea Aarrestad and Steffen Bahr and Jurgen Becker and Anne-Sophie Berthold and Richard J. Bonventre and Tomas E. Muller Bravo and Markus Diefenthaler and Zhen Dong and Nick Fritzsche and Amir Gholami and Ekaterina Govorkova and Kyle J Hazelwood and Christian Herwig and Babar Khan and Sehoon Kim and Thomas Klijnsma and Yaling Liu and Kin Ho Lo and Tri Nguyen and Gianantonio Pezzullo and Seyedramin Rasoulinezhad and Ryan A. Rivera and Kate Scholberg and Justin Selig and Sougata Sen and Dmitri Strukov and William Tang and Savannah Thais and Kai Lukas Unger and Ricardo Vilalta and Belinavon Krosigk and Thomas K. Warburton and Maria Acosta Flechas and Anthony Aportela and Thomas Calvet and Leonardo Cristella and Daniel Diaz and Caterina Doglioni and Maria Domenica Galati and Elham E Khoda and Farah Fahim and Davide Giri and Benjamin Hawks and Duc Hoang and Burt Holzman and Shih-Chieh Hsu and Sergo Jindariani and Iris Johnson and Raghav Kansal and Ryan Kastner and Erik Katsavounidis and Jeffrey Krupa and Pan Li and Sandeep Madireddy and Ethan Marx and Patrick McCormack and Andres Meza and Jovan Mitrevski and Mohammed Attia Mohammed and Farouk Mokhtar and Eric Moreno and Srishti Nagu and Rohin Narayan and Noah Palladino and Zhiqiang Que and Sang Eon Park and Subramanian Ramamoorthy and Dylan Rankin and Simon Rothman and Ashish Sharma and Sioni Summers and Pietro Vischia and Jean-Roch Vlimant and Olivia Weng},
journal= {arXiv preprint arXiv:2110.13041},
year = {2023}
}
Comments
66 pages, 13 figures, 5 tables