NDLI: Efficacy of memristive crossbars for neuromorphic processors

Content Provider	IEEE Xplore Digital Library
Author	Yakopcic, C. Hasan, R. Taha, T.M. McLean, M.R. Palmer, D.
Copyright Year	2014
Description	Author affiliation: Annapolis Microsyst., Annapolis, MD, USA (Palmer, D.) \|\| Univ. of Dayton, Dayton, OH, USA (Yakopcic, C.; Hasan, R.; Taha, T.M.) \|\| Lab. for Phys. Sci., College Park, MD, USA (McLean, M.R.)
Abstract	This paper describes memristor-based neuromorphic circuits for non-linear separable pattern recognition. We initially describe a memristor based neuron circuit and then show how multilayer neural networks can be constructed using this neuron circuit. These neuromorphic circuits are capable of learning both linearly and non-linearly separable logic functions. This paper presents the first study of applying neural network learning algorithms to these circuits in SPICE. Our simulations capture alternate current paths within the memristor crossbars and wire resistances that are essential to properly model in crossbar circuits. Our results show that neural network learning algorithms are able to train around these alternate current paths. Further, it was shown that neural networks can properly train the passive memristor-based crossbars without having to use virtual ground mode operational amplifiers as suggested in previous work. Our circuit requires in-situ training, but reduces the number of transistors required by the circuit by about 3 times and reduced the circuit power consumption almost 2 orders of magnitude compared to a virtual ground approach. The key impact of this study is the demonstration through low level circuit simulations that dense memristor crossbars can be effectively utilized to build neuromorphic processors.
Starting Page	15
Ending Page	20
File Size	4416860
Page Count	6
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889807
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Memristors Neurons Training Integrated circuit modeling SPICE Mathematical model Biological neural networks
Content Type	Text
Resource Type	Article

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