Cellular Neural Network

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Cellular Neural Network

(CNN) The CNN Universal Machine is a low cost, low power, extremely high speed supercomputer on a chip. It is at least 1000 times faster than equivalent DSP solutions of many complex image processing tasks. It is a stored program supercomputer where a complex sequence of image processing algorithms is programmed and downloaded into the chip, just like any digital computer. Because the entire computer is integrated into a chip, no signal leaves the chip until the image processing task is completed.

Although the CNN universal chip is based on analogue and logic operating principles, it has an on-chip analog-to-digital input-output interface so that at the system design and application perspective, it can be used as a digital component, just like a DSP. In particular, a development system is available for rapid design and prototyping. Moreover, a compiler, an operating system, and a user-friendly CNN high-level language, like the C language, have been developed which makes it easy to implement any image processing algorithm.

[Professor Leon Chua, University of California at Berkeley].
This article is provided by FOLDOC - Free Online Dictionary of Computing (foldoc.org)
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References in periodicals archive ?
Cheung, "Realization of a Digital Cellular Neural Network for Image Processing", in Proceedings of Third International Workshop on Cellular Neural Networks and Their Applications, Rome, Italy, 1994, pp.
The cellular neural network is a neural network with local interconnection and proposed firstly by Chua in 1988 [1].
Main obstruct in the modelling of features using Cellular Neural Network (CNN) (Mitchell et al.
Han, "Synchronization schemes for coupled identical YANg-YANg type fuzzy cellular neural networks," Communications in Nonlinear Science and Numerical Simulation, vol.
It is well known that the delayed cellular neural networks (DCNNs) have been successfully applied to many practical problems, such as signal and image processing, pattern recognition, and optimization.
When impulsive cellular neural network models are used for the solution of optimization problems, one of the fundamental issues in the design of a network is concerned with the existence of a unique globally exponentially stable equilibrium state of network (5a)-(5b).
Agarwal, "Fixed points and exponential stability for a stochastic neutral cellular neural network," Applied Mathematics Letters.
Park, "Synchronization of cellular neural networks of neutral type via dynamic feedback controller," Chaos, Solitons and Fractals, vol.
firstly introduced a cellular neural network (CNN) with fractional-order cells.
Liang, "Exponential stability analysis of stochastic delayed cellular neural network," Chaos, Solitons & Fractals, vol.
Yang, "A shunting inhibitory cellular neural network with continuously distributed delays of neutral type," Nonlinear Analysis: Real World Applications, vol.
Yang, "The global stability of fuzzy cellular neural network," IEEE Transactions on Circuits and Systems, vol.

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