Neural Encoding: Methodology and Design

Speaker:  Chenyuan Zhao

Host: MICS

Date: December 1 (Friday), 2017
Time: 2:30 PM - 3:30 PM
Location: Whittemore 654 (6th Floor Conference Room)

Abstract:

Von Neumann Bottleneck, which refers to the limited throughput between the CPU and memory, has already become the major factor hindering the technical advances of computing systems. In recent years, neuromorphic systems started to gain the increasing attentions as compact and energy-efficient computing platforms. As one of the most crucial components in the neuromorphic computing systems, neural encoder transforms the stimulus (input signals) into spike trains. In this talk, our research work on the spike-time-dependent encoding design, analysis, and optimization will be presented. The performance comparison among rate encoding, latency encoding, and temporal encoding will also be discussed.

Bio:

Chenyuan Zhao is a Ph.D. student in Electrical and Computer Engineering at Virginia Tech (VT). He received his B.S degree in Automation from Nanjing University of Posts & Telecommunications, China in 2007 and M.S degree in Communication and Information System from Jinan University, China in 2012. He has published 6 journal papers and 8 peer reviewed international conference papers during his Ph.D. study. He is also the recipient of ICCAD SRC student award and KU (University of Kansas) Robb award. His research interests include neuromorphic engineering, machine learning, analog and mixed signal integrated circuits design.