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A Low-Power CMOS Programmable CNN Cell and its Application to Stability of CNN with Opposite-Sign Templates

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Author(s): S. El-Din | A. K. Abol Seoud | A. El-Fahar

Journal: International Journal of Computer Science and Information Security
ISSN 1947-5500

Volume: 9;
Issue: 5;
Start page: 43;
Date: 2011;
Original page

Keywords: Cellular Neural Network | Low-power CNN | Opposite-Sign Template.

ABSTRACT
In this paper, a novel VLSI architecture adaptation of the Cellular Neural Network (CNN) paradigm is described. It is based on a combination of MOS transistors operating in weak inversion regime. This combination has enabled a CMOS implementation of a simplified version of the original CNN model with the main characteristics of low-power consumption. Digitally selectable template coefficients are employed and a local logic and memory are added into each cell providing a simple dual computing structure (analog and digital). A four-quadrant analog multiplier is used as a voltage controlled current source which is feeding from the weighting factors of the template elements. The main feature of the multiplier is the high value of the weight voltage range which varies between the ground voltage and the supply voltage. A simulation example for stability of a class of nonreciprocal cellular neural network with opposite-sign template is presented.
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