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Efficient and resilient Machine learning in edge computing

Date: 2018-05-23
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Efficient and resilient Machine learning in edge computing

I. Details of the lecture 

Topic: Efficient and resilient Machine learning in edge computing

Time: 14:00, May 25th, 2018

Location: B314, Institute of Microelectronics, Tsinghua University

Speaker: Li Jiang  Associate  professor in the Dept. of computer science and engineering, Shanghai Jiao Tong University

II. Introduction to the speaker 

Li Jiang is an associate professor in the Dept. of computer science and engineering, Shanghai Jiao Tong University. He received the B.S. degree from the Dept. of computer science and engineering, Shanghai Jiao Tong University in 2007, the Mphil and the Ph.D. degree from the Dept. of computer science and engineering, the Chinese University of Hong Kong in 2010 and 2013, respectively. He received a best paper nomination in ICCAD, and got best Ph.D. Dissertation award in ATS 2014, and was in the final list of TTTC’s E. J. McCluskey Doctoral Thesis Award. His current research interests include computer architecture with emerging technologies and applications, design automation for/with machine learning, reliability and performance enhancement for hardware system.

III. Content of lecture 

Emerging device, such as ReRAM, is highly efficient to accelerate various DNN based applications, such as approximate computing. However, in device level, these emerging devices suffer from low yield due to various defects and process variations. In application level, on the other hand, the complex distributions of the input data not only incur unacceptable errors, but also degrade the invocation of accelerators. To mitigate above challenges, this talk first discusses application-level solutions to enhance the yield and resilience of the ReRAM based accelerators, and then present novel training methods to enhance the efficiency of DNN-based approximate computing.


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