Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics
Abstract
The next generation of particle physics experiments will face an era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing these data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning, silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing toward a successful transition to the data frontier of fundamental science.
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Authors: Julia Gonski, Jenni Ott, Shiva Abbaszadeh, Sagar Addepalli, Matteo Cremonesi, Jennet Elizabeth Dickinson, Giuseppe Di Guglielmo, Erdem Yigit Ertorer, Lindsey Gray, Ryan T. Herbst, Christian Herwig, Tae Min Hong, Benedikt Maier, Maryam Bayat Makou, David C. Miller, Mark S. Neubauer, Cristián Peña, Dylan Rankin, Seon-Hee, Seo, A. Tapper, Audrey Corbeil Therrien, H. Xu, K. Yoshihara, N. Akchurin, Carlos Arguelles, S. Bhattacharya, Lorenzo Borella, C. Boutan, T. Braine, James Brau, Marco Carminati, Antonio Chahine, K. Choi, Talal Ahmed Chowdhury, Seokju Chung, Ryan Coffee, Alberto Coppi, Mariarosaria D’Alfonso, Abhilasha Dave, Chance Desmet, Angela Di Fulvio, K. F. Di Petrillo, Javier Mauricio Duarte, Auralee Edelen, Jan Eysermans, Y. Feng, Emmett Forrestel, Julián García Pardiñas, L. Gastaldo, Lino Gerlach, Abarajithan Gnaneswaran, L. Gouskos, Ekaterina Govorkova, Carl R. Grace, Christopher Grant, Philip Harris, Ciaran Hasnip, T. Heim, Abraham Thomas Holtermann, Gian Michele Innocenti, K. Ishidoshiro, Miaochen Jin, J. P. Johnson, Stephen Jones, Andreas Werner Jung, N. Kamp, G. Karagiorgi, Ryan Kastner, Doojin Kim, Kyoungchul Kong, Katie Kudela, Bo-Cheng Charles Lai, Jelena Lalic, T. Lam, Jeffrey Lazar, Y.-T. Lai, Zepeng Li, Haoyun Liu, Qibin Liu, Vladimir Lončar, Zhenghua Ma, Luca Macchiarulo, C. Madrid, Prashansa Mukim, Victoria Nguyen, S. B. Oh, Isobel Ojalvo, H. Ozaki, S. Pagan Griso, S. Parajuli, Myeonghun Park, Benjamin Parpillon, Christoph Paus, Sara Pozzi, Ema Puljak, Benjamin Ramhorst, Amy Roberts, Larry L. Ruckman
Institutions: University of Padua, University of Pittsburgh, University of Pennsylvania, University of Chicago, University of Washington, Northwestern University, University of Colorado Denver, Columbia University, University of California San Diego, Imperial College London, Harvard University, Brown University, University of Michigan, Cornell University, Boston University, University of Kansas, Heidelberg University, University of Hawaiʻi at Mānoa, National Yang Ming Chiao Tung University, UCLouvain, Tohoku University, Massachusetts Institute of Technology, Politecnico di Milano, Université de Sherbrooke, ETH Zurich, University of California, Santa Cruz, High Energy Accelerator Research Organization, Purdue University West Lafayette, University of Oregon, Princeton University, Virginia Tech, Istituto Nazionale di Fisica Nucleare, Brookhaven National Laboratory, European Organization for Nuclear Research, Texas Tech University, University of South Dakota, Carnegie Mellon University, Lawrence Berkeley National Laboratory, Fermi National Accelerator Laboratory, SLAC National Accelerator Laboratory, Seoul National University of Science and Technology, Tokyo University of Science, Southern Methodist University, Pacific Northwest National Laboratory, Nalu Scientific (United States)