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胡小波 :In-Memory Computing for Machine Learning Applications and Beyond--- A Cross-Layer Design Case Study of Ferroelectric FETs
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胡小波 :In-Memory Computing for Machine Learning Applications and Beyond--- A Cross-Layer Design Case Study of Ferroelectric FETs
  点击数: 27 发布时间:2019-10-22

报告时间:2019102410:0011:30

报告地点:翡翠湖校区科教楼第五会议室

人:胡小波教授

工作单位: Department of Computer Science and Engineering at the University of Notre Dame, USA

举办单位:正规赌博十大平台/微电子学院

报告人简介:X. Sharon Hu is a professor in the department of Computer Science and Engineering at the University of Notre Dame, USA. Her research interests include low-power system design, circuit and architecture design with emerging technologies, real-time embedded systems and hardware-software co-design. She has published more than 300 papers in these areas. Some of her recognitions include the Best Paper Award from the Design Automation Conference and from the International Symposium on Low Power Electronics and Design, and the NSF CAREER award. She has participated in several large industry and government sponsored center-level projects and is a theme leader in an NSF/SRC E2CDA project. She served as the General Chair and Program Chair of Design Automation Conference and is the Program Chair of 2019 IEEE Real-Time Systems Symposium. She also served as Associate Editor for IEEE Transactions on VLSI, ACM Transactions on Design Automation of Electronic Systems, etc. and is an Associate Editor of ACM Transactions on Cyber-Physical Systems. X. Sharon Hu is a Fellow of the IEEE.

报告简介:Data transfer between a processor and memory is a major bottleneck in improving application-level performance. This is particularly the case for data intensive tasks such as some machine learning applications. In-memory computing, where certain data processing is performed in memory, could be an effective solution to address this bottleneck. Consequently, compact, low-power, fast and non-volatile in-memory computing is highly desirable. This talk presents a cross-layer effort of designing in-memory computing modules based on ferroelectric FETs, an emerging, non-volatile device. An FeFET is made by integrating a ferroelectric material layer in the gate stack of a MOSFET, and can behave as both a transistor and a non-volatile storage element. This unique property enables area efficient and low-power finely integrated logic and memory. Novel circuits/architectures based on FeFETs to accomplish computing in memory, content addressable memory and crossbar arrays will be elaborated. Application-level benefits, particularly for machine learning, in comparison with other alternative technologies will be discussed.

 
 
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学院地址:合肥市丹霞路485号翡翠湖校区翡翠科教楼十七楼
学院电话:0551-62919106 传真:0551-62919106 邮编:230009

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