Reconfigurable Neuromorphic Computing System with Memristor-Based Synapse Design

abstract

• 2013, Springer Science+Business Media New York. Conventional CMOS technology is slowly approaching its physical limitations and researchers are increasingly utilizing nanotechnology to both extend CMOS capabilities and to explore potential replacements. Novel memristive systems continue to attract growing attention since their reported physical realization by HP in 2008. Unique characteristics like non-volatility, re-configurability, and analog storage properties make memristors a very promising candidate for the realization of artificial neural systems. In this work, we propose a memristor-based design of bidirectional transmission excitation/inhibition synapses and implement a neuromorphic computing system based on our proposed synapse designs. The robustness of our system is also evaluated by considering the actual manufacturing variability with emphasis on process variation.

authors

• Huang, Tingwen

published proceedings

• NEURAL PROCESSING LETTERS

author list (cited authors)

• Liu, B., Chen, Y., Wysocki, B., & Huang, T.

citation count

• 23

complete list of authors

• Liu, Beiye||Chen, Yiran||Wysocki, Bryant||Huang, Tingwen

publication date

• April 2015

publisher

• Springer Nature  Publisher

published in

• Neural Processing Letters  Journal

keywords

• Bidirectional Synapse
• Hopfield Network
• Memristor
• Pattern Recognition

Digital Object Identifier (DOI)

• 10.1007/s11063-013-9315-8

start page

• 159

end page

• 167

volume

• 41

issue

• 2

URL

• http://dx.doi.org/10.1007/s11063-013-9315-8

user-defined tag

• 7 Affordable and Clean Energy