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[青促会物理论坛(52)] 基于新型电子突触器件的类脑计算研究
时间: 2019年08月19日 10:00
地点: 新萄京科学院物理研究所 M255会议室
报告人: Prof. Huaqiang Wu (吴华强)

Institute for Microelectronics, Tsinghua University

Recently, computation in memory becomes very hot due to the urgent needs of high computing efficiency in artificial intelligence applications. In contrast to von-Neumann architecture, computation in memory technology avoids the data movement between CPU/GPU and memory which could greatly reduce the power consumption. Memristor is one ideal device which could not only store information with multi-bits, but also conduct computing using ohm’s law. To make the best use of the memristor in neuromorphic systems, a memristor-friendly architecture and the software-hardware collaborative design methods are essential, and the key problem is how to utilize the memristor’s analog behavior. We have designed a generic memristor crossbar based architecture for convolutional neural networks and perceptions, which take full consideration of the analog characteristics of memristors. Furthermore, we have proposed an online learning algorithm for memristor based neuromorphic systems which overcomes the variation of memristor cells and endue the system the ability of reinforcement learning based on memristor’s analog behavior. 

报告人简介:

吴华强, 清华大学微纳电子系,教授,副系主任,清华大学微纳加工平台主任,北京市未来芯片技术高精尖创新中心副主任。2000年毕业于清华大学材料科学与工程系,获得工学学士学位;同年获清华大学经济管理学院管理学士学位(双学位)。2005年在美国康奈尔大学(Cornell University)电子与计算机工程学院获工学博士学位。随后在美国AMD公司和Spansion公司非易失性存储器研发中心任高级研究员和主任研究员,从事先进非易失性存储器的架构、器件和工艺研究。2009年,加入清华大学微电子学研究所,研究领域为新型半导体存储器及基于新型器件的类脑计算研究。先后负责多项自然科学基金、863、973和重点研发计划多项课题。在Nature Communications, Nature Electronics, Nano Letters, Advanced Materials, Advanced Functional Materials, Scientific Reports等期刊和国际会议发表论文100余篇,获得美国授权发明专利21项,获得中国授权发明专利32项。

联系人:葛琛 (82649478, gechen@iphy.ac.cn)

本次活动由中科院物理所青促会小组主办


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