arXiv 物理· Julia Gonski, Jennifer Ott, Shiva Abbaszadeh, Sagar Addepalli, Matteo Cremonesi, Jennet Dickinson, Giuseppe Di Guglielmo, Yigit Erdem Ertorer, Lindsey Gray, Ryan Herbst, Christian Herwig, Tae Min Hong, Benedikt Maier, Maryam Bayat Makou, David W. Miller, Mark S. Neubauer, Cristian Pena, Dylan Rankin, Seon-Hee Seo, Giordon Stark, Alexander Tapper, Audrey Corbeil Therrien, Ioannis Xiotidis, Keisuke Yoshihara, Nural Akchurin, Carlos Arguelles, Saptaparna Bhattacharya, Lorenzo Borella, Christian Boutan, Tom Braine, James Brau, Marco Carminati, Antonio Chahine, Yuan-Tang Chou, Talal Ahmed Chowdhury, Seokju Chung, Ryan Coffee, Alberto Coppi, Mariarosaria D'Alfonso, Abhilasha Dave, Chance Desmet, Angela Di Fulvio, Karri DiPetrillo, Javier Duarte, Auralee Edelen, Jan Eysermans, Yongbin Feng, Emmett Forrestel, Julian Garcia Pardinas, Loredana Gastaldo, Lino Gerlach, Abarajithan Gnaneswaran, Loukas Gouskos, Katya Govorkova, Carl Grace, Christopher Grant, Philip Harris, Ciaran Hasnip, Timon Heim, Abraham Holtermann, Gian Michele Innocenti, Koji Ishidoshiro, Miaochen Jin, Jyothisraj Johnson, Stephen Jones, Andreas Jung, Nicholas Kamp, Georgia Karagiorgi, Ryan Kastner, Doojin Kim, Kyoungchul Kong, Katie Kudela, Bo-Cheng Lai, Yun-Tsung Lai, Jelena Lalic, Tommy Lam, Jeffrey Lazar, Aobo Li, Zepeng Li, Haoyun Liu, Qibin Liu, Vladimir Loncar, Zhenghua Ma, Luca Macchiarulo, Christopher Madrid, Prashansa Mukim, Victoria Nguyen, Sungbin Oh, Isobel Ojalvo, Hideyoshi Ozaki, Simone Pagan Griso, Santosh Parajuli, Myeonghun Park, Benjamin Parpillon, Christoph Paus, Sara Pozzi, Ema Puljak, Benjamin Ramhorst, Amy Roberts, Larry Ruckman, Sebastian Schmitt, Kate Scholberg, Noah Singer, Eluned Anne Smith, Alexandre Sousa, Michael Spannowsky, Kaito Sugizaki, Sioni Summers, Yanwen Sun, Daniel Tapia Takaki, Antonino Tumeo, Caterina Vernieri, Belina von Krosigk, Yash Vora, Linyan Wan, Michael H. L. S. Wang, Amanda Weinstein, Andy White, Simon Williams, Liangyu Wu, Felix Yu·· 3 天前AI 评分38
面向粒子物理的异构、边缘与量子硬件机器学习（ML-HEQUPP）
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
AI 导读
一份 73 页白皮书提出 ML-HEQUPP 社区愿景，为粒子物理实验梳理异构加速器系统、边缘低功耗低延迟器件、可重构硬件、低温与高辐射环境读出及模拟计算等硬件级机器学习研发方向。下一代实验将面临前所未有的数据速率与体量，需要实时推理、智能数据缩减和超越现有能力的处理架构。
来源：arXiv 物理 · arxiv.org