NDLI: Analog implementation of ANN with inherent quadratic nonlinearity of the synapses

Content Provider	IEEE Xplore Digital Library
Author	Milev, M. Hristov, M.
Copyright Year	1990
Abstract	In real-life applications of multilayer neural networks, the scale of integration, processing speed, and manufacturability are of key importance. A simple analog-signal synapse model is implemented on a standard 0.35 /spl mu/m CMOS process requiring no floating-gate capability. A neural-matrix of 2176 analog current-mode synapses arranged in eight layers of 16 neurons with 16 inputs each is constructed for the purpose of a fingerprint feature extraction application. Synapse weights are stored on the analog storage capacitors, and synapse nonlinearity with respect to weight is investigated. The capability of the synapse to operate in feedforward and learning modes is studied and demonstrated. The effect of the synapse's inherent quadratic nonlinearity on learning convergence and on the optimization of vector direction is analyzed. Transistor-level analog simulations verify the hardware circuit. System-level MatLab simulations verify the synapse mathematical model. The conclusion reached is that the proposed implementation is very suitable for large-scale artificial neural networks - especially if on-chip integration with other products on a standard CMOS process is required.
Sponsorship	IEEE Computational Intelligence Society
Starting Page	1187
Ending Page	1200
Page Count	14
File Size	577934
File Format	PDF
ISSN	10459227
Volume Number	14
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Multi-layer neural network Mathematical model CMOS process Circuit simulation Neural networks Manufacturing processes Semiconductor device modeling Neurons Fingerprint recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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