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2019-03-14

胡晓波教授IAC委员聘任仪式暨专题讲座成功举办

               2019312日上午9点半,国际电气与电子工程师协会电子设计自动化委员会IEEE CEDA)的杰出讲师(Distinguished Lecturer)、美国圣母大学(University of Notre Dame)计算机科学与工程系(Department of Computer Science and Engineering )胡晓波教授(X. Sharon Hu)到访北京大学微纳电子学系。北京大学信息与工程科学部主任黄如院士主持接待,微纳电子学系系主任蔡一茂教授,党支部书记王源教授等相关研究方向的四十余名师生陪同参会。

                                                   

黄如院士首先对胡晓波教授的到来表示了热烈欢迎,并为胡晓波教授颁发了“北京大学微纳电子学系国际顾问委员会委员”的聘书;聘任仪式之后,胡晓波教授做了题为《Exploiting Ferroelectric FETs:From In-Memory Computing to Machine Learning and Beyond》的演讲报告。随后,与会者与胡教授进行了深入、热烈的讨论与交流。

                                                  

 

Exploiting Ferroelectric FETs: From In-Memory Computing to Machine Learning and Beyond

The inevitable slowdown of the CMOS scaling trend has fueled an explosion of research endeavors in finding a CMOS replacement. However, recent studies suggest that many of the emerging devices being investigated, if used as simple drop-in replacement for MOSFETs, may only achieve speedups that mirror historical trends in the best case. The consensus from the community is that cross-layer efforts are essential in combating the CMOS scaling challenge with emerging devices. This talk presents such an effort centered around a particular emerging device, ferroelectric FETs (FeFETs).

An FeFET is made by integrating a ferroelectric material layer in the gate stack of a MOSFET. It is a non-volatile device that can behave as both a transistor and a storage element. This unique property of FeFETs enables area efficient and low-power combined logic and memory, which are desirable for many data analytic and machine learning applications. This presentation will elaborate novel circuits/architectures based on FeFETs to accomplish computing in memory, ternary content addressable memory (TCAM) and crossbar arrays. Application-level benefits, particularly for machine learning, in comparison with other alternative technologies will be discussed.


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