GraphChallenge.org Sparse Deep Neural Network Performance
MIT Lincoln Laboratory Supercomputing Center（麻省理工学院林肯实验室超级计算中心）；MIT Computer Science & AI Laboratory（麻省理工学院计算机科学与人工智能实验室）；MIT Mathematics Department（麻省理工学院数学系）；MIT Dept. of Earth, Atmospheric, & Planetary Sciences（麻省理工学院地球、大气与行星科学系）
Jeremy Kepner, Simon Alford, Vijay Gadepally, Michael Jones, Lauren Milechin, Albert Reuther, Ryan Robinett, Sid Samsi
备注：
7 pages, 7 figures, 80 references, to be submitted to IEEE HPEC 2020. This work reports new updated results on prior work reported in arXiv:1909.05631. arXiv admin note: substantial text overlap with arXiv:1807.03165, arXiv:1708.02937. arXiv admin note: text overlap with arXiv:2003.09269
The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective of emerging sparse AI systems. The sparse DNN challenge is based on a mathematically well-defined DNN inference computation and can be implemented in any programming environment. In 2019 several sparse DNN challenge submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art sparse DNN execution time, $T_{\rm DNN}$, is a strong function of the number of DNN operations performed, $N_{\rm op}$. The sparse DNN challenge provides a clear picture of current sparse DNN systems and underscores the need for new innovations to achieve high performance on very large sparse DNNs.
A deep dictionary network-based foundation model for ultra-low-dose CT denoising
Yanshan University（燕山大学）；Capital Medical University（首都医科大学）；Beijing Friendship Hospital（北京友谊医院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）
Sparsely Grouped Input Variables for Neural Networks
Yale Child Study Center, School of Medicine, Yale University（耶鲁大学医学院耶鲁儿童研究中心）；Paul G. Allen School of Computer Science and Engineering, University of Washington（华盛顿大学保罗·G·艾伦计算机科学与工程学院）；Seattle Children’s Research Institute（西雅图儿童研究所）；School of Medicine, University of Washington（华盛顿大学医学院）
