AUTHOR=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 , 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 , von Krosigk Belina , Wang Shen , Warburton Thomas K. TITLE=Applications and Techniques for Fast Machine Learning in Science JOURNAL=Frontiers in Big Data VOLUME=5 YEAR=2022 URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2022.787421 DOI=10.3389/fdata.2022.787421 ISSN=2624-909X 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.