Seminar of Institute of Information Systems and Applications

Abstract

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. The energy efficiency (power efficiency) is one of the major considerations of processing big data with large scale neural networks. RRAM (or memristor) and its crossbar structure provide a promising solution to perform the computation on memory and can significantly boost the energy/power efficiency of big data applications like neuromorphic computation and Deep Learning. In this talk, Dr. Wang will introduce energy efficient implementation of neural networks by taking advantage of the emerging RRAM technique. Dr. Wang will provide some recent results on kinds of energy efficient NNs based on RRAM, such as CNN, SNN, DNN and etc on a simulation platform. To verify the above designs, Dr. Wang will introduce some recent progress on chip design and planning for RRAM-based neural networks.

Bio

Dr. Yu Wang is an Associate Prof. in EE Dept, Tsinghua University. Dr. Wang's research mainly focuses on brain inspired computing, application specific hardware computing, and parallel circuit analysis. Dr. Wang has authored and coauthored over 130 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 2012-2015, and serves as TPC member in many important conferences (DAC/ICCAD/DATE, FPGA, ISLPED, etc).

All faculty and students are welcome to join the lecture