NDLI: Stochastic Gradient Descent Inspired Training Technique for a CMOS/Nano Memristive Trainable Threshold Gate Array

Content Provider	IEEE Xplore Digital Library
Author	Manem, H. Rajendran, J. Rose, G.S.
Copyright Year	2004
Abstract	Neuromorphic computing is an attractive avenue of research for processing and learning complex real-world data. With technology migration into nano and molecular scales several area and power efficient approaches to the design and implementation of artificial neural networks have been proposed. The discovery of the memristor has further enabled the realization of denser nanoscale logic and memory systems by facilitating the implementation of multilevel logic. Specifically, the innate reconfigurability of memristors can be exploited to realize synapses in artificial neural networks. This work focuses on the development of a variation-tolerant training methodology to efficiently reconfigure memristive synapses in a Trainable Threshold Gate Array (TTGA) system. The training process is inspired from the gradient descent machine learning algorithm commonly used to train artificial threshold neural networks, perceptrons. The design and CMOS/Nano implementation of the TTGA system from trainable perceptron based threshold gates is detailed and results are provided to showcase the training process and performance characteristics of the proposed system. Also shown are the results for training a 1T1M (1 Transistor and 1 Memristor) multilevel memristive memory and its performance characteristics.
Sponsorship	IEEE Circuits and Systems Society
Starting Page	1051
Ending Page	1060
Page Count	10
File Size	1350363
File Format	PDF
ISSN	15498328
Volume Number	59
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Memristors Training CMOS integrated circuits Logic gates Hardware Boolean functions Vectors VLSI Digital integrated circuits machine learning memristor nanoelectronics neural networks
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

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