"Energy Efficient Neural Networks for Big Data Analytics"

Yu Wang, Associate Professor, EE, Tsinghua University

November 7th (Friday), 10:00am
Harold Frank Hall (HFH), Room 4164 (ECE Conf. Rm)

The world is experiencing a data revolution to discover knowledge in big data. Large scale neural networks are one of the mainstream tools of big data analytics. Processing big data with large scale neural networks includes two phases: the operation phase and the training phase. The energy efficiency (power efficiency) is one of the major considerations of the operation phase. Meanwhile, huge computing power is required to support the training phase. In this talk, Dr. Wang will introduce an energy efficient implementation of neural networks’ operation phase by taking advantage of the emerging memristor (ReRAM) technique. Then Dr. Wang will show some recent results on exploring the computing power of GPUs for big data analytics and demonstrate efficient GPU implementation of the training phase of large scale recurrent neural networks (RNNs) and deep neural networks (DNNs).

About Yu Wang:

photo of yu wang Dr. Yu Wang is an Associate Prof. in EE Dept, Tsinghua University. Dr. Wang's research mainly focuses on parallel circuit analysis, brain inspired computing, application specific hardware computing (especially on the Brain related problems), and power and reliability aware system design methodology. Dr. Wang has authored and coauthored over 100 papers in refereed journals and conferences. He is the recipient of IBM X10 Faculty Award in 2010, Best Paper Award in ISVLSI 2012, Best Poster Award in HEART 2012, and 6 Best Paper Nomination in ASPDAC/CODES/ISLPED. He serves as the Associate Editor for IEEE Trans on CAD, Journal of Circuits, Systems, and Computers. He is the TPC Co-Chair of ICFPT 2011, Finance Chair of ISLPED 2012/2013/2014, and serves as TPC member in many important conferences (DAC, FPGA, DATE, ASPDAC, ISLPED, ISQED, ICFPT, ISVLSI, etc).

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