加入收藏夹
联系我们
关于本站
个人主页
西电导航
西安电子科技大学
    当前位置:首页>>学术讲座
From Matrix to Tensor: Algorithm and Hardware Co-Design for Energy-Efficient Deep Learning
时间:2019-06-24 08:45    点击:   所属单位:通信工程学院
讲座名称 From Matrix to Tensor: Algorithm and Hardware Co-Design for Energy-Efficient Deep Learning
讲座时间 2019-06-27 10:30:00
讲座地点 西电北校区新科技楼1012会议室
讲座人 袁博
讲座人介绍
Dr. Bo Yuan is currently the assistant professor in the Department of Electrical and Computer Engineering in Rutgers University. Before that, he was with City University of New York from 2015-2018. Dr. Bo Yuan received his bachelor and master degrees from Nanjing University, China in 2007 and 2010, respectively. He received his PhD degree from Department of Electrical and Computer Engineering at University of Minnesota, Twin Cities in 2015.
His research interests include algorithm and hardware co-design and implementation for machine learning and signal processing systems, error-resilient low-cost computing techniques for embedded and IoT systems and machine learning for domain-specific applications. He is the recipient of Global Research Competition Finalist Award in Broadcom Corporation. Dr. Yuan serves as technical committee track chair and technical committee member for several IEEE/ACM. He is the associated editor of Springer Journal of Signal Processing System.
讲座内容
In the emerging artificial intelligence era, deep neural networks (DNNs), a.k.a. deep learning, have gained unprecedented success in various applications. However, DNNs are usually storage intensive, computation intensive and very energy consuming, thereby posing severe challenges on the future wide deployment in many application scenarios, especially for the resource-constraint low-power IoT application and embedded systems.
In this talk, I will introduce my recent algorithm/hardware co-design works for energy-efficient DNN (MICRO'17,MICRO'18, ISCA'19).  First, I will show the use of low displacement rank (LDR) matrices can enable the construction of low-complexity DNN models as well as the corresponding energy-efficient DNN hardware accelerators. In the second part of my talk, I will show the benefit of using permuted diagonal matrix, as another type of structured and sparse matrix, for the energy-efficient DNN hardware design. Finally, I will introduce the benefits of tensor decomposition for DNN design and the corresponding high-performance DNN accelerator.
转载请注明出处:西安电子科技大学学术信息网
如果您有学术信息或学术动态,欢迎投稿。我们将在第一时间确认并收录,投稿邮箱: meeting@xidian.edu.cn
Copyright © 2011-2019 西安电子科技大学 
开发维护:电子工程学院网络信息中心  管理员:meeting@xidian.edu.cn 站长统计: