Frontiers in Big Data (Apr 2022)
Applications and Techniques for Fast Machine Learning in Science
- Allison McCarn Deiana,
- Nhan Tran,
- Nhan Tran,
- Joshua Agar,
- Michaela Blott,
- Giuseppe Di Guglielmo,
- Javier Duarte,
- Philip Harris,
- Scott Hauck,
- Mia Liu,
- Mark S. Neubauer,
- Jennifer Ngadiuba,
- Seda Ogrenci-Memik,
- Maurizio Pierini,
- Thea Aarrestad,
- Steffen Bähr,
- Jürgen Becker,
- Anne-Sophie Berthold,
- Richard J. Bonventre,
- Tomás E. Müller Bravo,
- Markus Diefenthaler,
- Zhen Dong,
- Nick Fritzsche,
- Amir Gholami,
- Ekaterina Govorkova,
- Dongning Guo,
- Kyle J. Hazelwood,
- Christian Herwig,
- Babar Khan,
- Sehoon Kim,
- Thomas Klijnsma,
- Yaling Liu,
- Kin Ho Lo,
- Tri Nguyen,
- Gianantonio Pezzullo,
- Seyedramin Rasoulinezhad,
- Ryan A. Rivera,
- Kate Scholberg,
- Justin Selig,
- Sougata Sen,
- Dmitri Strukov,
- William Tang,
- Savannah Thais,
- Kai Lukas Unger,
- Ricardo Vilalta,
- Belina von Krosigk,
- Belina von Krosigk,
- Shen Wang,
- Thomas K. Warburton
Affiliations
- Allison McCarn Deiana
- Department of Physics, Southern Methodist University, Dallas, TX, United States
- Nhan Tran
- Fermi National Accelerator Laboratory, Batavia, IL, United States
- Nhan Tran
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
- Joshua Agar
- Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA, United States
- Michaela Blott
- Xilinx Research, Dublin, Ireland
- Giuseppe Di Guglielmo
- Department of Computer Science, Columbia University, New York, NY, United States
- Javier Duarte
- Department of Physics, University of California, San Diego, San Diego, CA, United States
- Philip Harris
- Massachusetts Institute of Technology, Cambridge, MA, United States
- Scott Hauck
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
- Mia Liu
- 0Department of Physics and Astronomy, Purdue University, West Lafayette, IN, United States
- Mark S. Neubauer
- 1Department of Physics, University of Illinois Urbana-Champaign, Champaign, IL, United States
- Jennifer Ngadiuba
- Fermi National Accelerator Laboratory, Batavia, IL, United States
- Seda Ogrenci-Memik
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
- Maurizio Pierini
- 2European Organization for Nuclear Research (CERN), Meyrin, Switzerland
- Thea Aarrestad
- 2European Organization for Nuclear Research (CERN), Meyrin, Switzerland
- Steffen Bähr
- 3Karlsruhe Institute of Technology, Karlsruhe, Germany
- Jürgen Becker
- 3Karlsruhe Institute of Technology, Karlsruhe, Germany
- Anne-Sophie Berthold
- 4Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany
- Richard J. Bonventre
- 5Lawrence Berkeley National Laboratory, Berkeley, CA, United States
- Tomás E. Müller Bravo
- 6Department of Physics and Astronomy, University of Southampton, Southampton, United Kingdom
- Markus Diefenthaler
- 7Thomas Jefferson National Accelerator Facility, Newport News, VA, United States
- Zhen Dong
- 8Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
- Nick Fritzsche
- 4Institute of Nuclear and Particle Physics, Technische Universität Dresden, Dresden, Germany
- Amir Gholami
- 8Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
- Ekaterina Govorkova
- 2European Organization for Nuclear Research (CERN), Meyrin, Switzerland
- Dongning Guo
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
- Kyle J. Hazelwood
- Fermi National Accelerator Laboratory, Batavia, IL, United States
- Christian Herwig
- Fermi National Accelerator Laboratory, Batavia, IL, United States
- Babar Khan
- 9Department of Computer Science, Technical University Darmstadt, Darmstadt, Germany
- Sehoon Kim
- 8Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
- Thomas Klijnsma
- Fermi National Accelerator Laboratory, Batavia, IL, United States
- Yaling Liu
- 0Department of Bioengineering, Lehigh University, Bethlehem, PA, United States
- Kin Ho Lo
- 1Department of Physics, University of Florida, Gainesville, FL, United States
- Tri Nguyen
- Massachusetts Institute of Technology, Cambridge, MA, United States
- Gianantonio Pezzullo
- 2Department of Physics, Yale University, New Haven, CT, United States
- Seyedramin Rasoulinezhad
- 3Department of Engineering and IT, University of Sydney, Camperdown, NSW, Australia
- Ryan A. Rivera
- Fermi National Accelerator Laboratory, Batavia, IL, United States
- Kate Scholberg
- 4Department of Physics, Duke University, Durham, NC, United States
- Justin Selig
- 5Cerebras Systems, Sunnyvale, CA, United States
- Sougata Sen
- 6Birla Institute of Technology and Science, Pilani, India
- Dmitri Strukov
- 7Department of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, United States
- William Tang
- 8Department of Physics, Princeton University, Princeton, NJ, United States
- Savannah Thais
- 8Department of Physics, Princeton University, Princeton, NJ, United States
- Kai Lukas Unger
- 3Karlsruhe Institute of Technology, Karlsruhe, Germany
- Ricardo Vilalta
- 9Department of Computer Science, University of Houston, Houston, TX, United States
- Belina von Krosigk
- 3Karlsruhe Institute of Technology, Karlsruhe, Germany
- Belina von Krosigk
- 0Department of Physics, Universität Hamburg, Hamburg, Germany
- Shen Wang
- 1Department of Physics, University of Florida, Gainesville, FL, United States
- Thomas K. Warburton
- 1Department of Physics and Astronomy, Iowa State University, Ames, IA, United States
- DOI
- https://doi.org/10.3389/fdata.2022.787421
- Journal volume & issue
-
Vol. 5
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.
Keywords
- machine learning for science
- big data
- particle physics
- codesign
- coprocessors
- heterogeneous computing
