11月20日（12周周二）在开发区校区教学楼B-107教室将举行两场报告：
第一场：
报告题目：Efficient and Scalable Deep Learning: An Interactive Play Between Software and Hardware.
报告时间：14:00-15:00
报告人：美国杜克大学李海教授，ACM distinguishes member，IEEE senior member，
第二场：
报告题目：战争形态的演变与国防技术发展
报告时间：15:00-16:00
报告人：中国兵器北方信息集团，苏彦峥研究员
欢迎各位老师和同学积极参加。
附：
Title： Efficient and Scalable Deep Learning: An Interactive Play Between Software and Hardware
Abstract： Following technology advances in high performance computation systems and fast growth of data acquisition, machine learning, especially deep learning, made remarkable success in many research areas and applications. Such a success, to a great extent, is enabled by developing large-scale deep neural networks (DNN) that learn from a huge volume of data. The deployment of such a big model, however, is both computation-intensive and memory-intensive. Though the research on hardware acceleration for neural network has been extensively studied, the progress of hardware development still falls far behind the upscaling of DNN models at soft-ware level. We envision that hardware/software co-design for accelerating and scaling up deep neural networks is necessary. In this work, I will start with the trends of machine learning study in academia and industry, followed by our study on DNN’s efficiency and scalability improvement in both inference and learning, demonstrating an interactive play between software and hardware.
Biography
Hai “Helen” Li received the B.S. and M.S. degrees from Tsinghua University, Beijing, China, and the Ph.D. degree from the Department of Electrical and Computer Engineering, Purdue University, USA. She is currently Clare Boothe Luce Associate Professor with the Department of Electrical and Computer Engineering at Duke University, USA. She has authored or co-authored over 200 technical papers published in peer-reviewed journals and conferences and holds 70+ granted U.S. patents. She authored a book entitled Nonvolatile Memory Design: Magnetic, Resistive, and Phase Changing (CRC Press, 2011). Her current research interests include memory design and architecture, deep learning and neuromorphic systems, and software/hardware co-design. Dr. Li has served and serves as Associate Editor of several IEEE and ACM journals, organization committee and technical program committee members for over 30 international conference series. She received the NSF CAREER Award (2012), the DARPA YFA Award (2013), TUM-IAS Hans Fisher Fellowship (2017), seven best paper awards and seven best paper nominations. Dr. Li is a senior member of IEEE and a distinguished member of ACM. She is a distinguished speaker of ACM (2017-2020), and a distinguished lecture of IEEE CAS society (2018-2019).
