RRAM Devices for Large Neuromorphic Systems




Abstract: Neuromorphic engineering holds promise to mimic the ability of the brain to perform fuzzy, fault-tolerant and stochastic computation, without sacrificing its area/power efficiency. In this paper, based on a scaling analysis, we determine the programming requirements of RRAM-like devices that could be used to fabricate such systems whose power and area approach that of the human brain.

Bio: Bipin Rajendran received the B.Tech degree in Instrumentation Engineering from Indian Institute of Technology, Kharagpur in 2000 and M.S and Ph.D degree in Electrical Engineering from Stanford University in 2003 and 2006 respectively. Currently, he is an Assistant Professor in the Department of Electrical Engineering at I.I.T. Bombay, India. His current research focus is on nanoscale devices for non-volatile memories and neuromorphic computation.