Cellular Neural Network


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Cellular Neural Network

(architecture)
(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].
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References in periodicals archive ?
Liang, "Exponential stability analysis of stochastic delayed cellular neural network," Chaos, Solitons & Fractals, vol.
Agarwal, "Fixed points and exponential stability for a stochastic neutral cellular neural network," Applied Mathematics Letters, vol.
1996), Cellular Neural Network (Orovas, Austin 1997) and Multiple Attractor Cellular Automata (Sikdar et al.
Researchers at Berkeley University in California have developed a hybrid analogue/digital computer called CNN - Cellular Neural Network - which they claim is three times faster than anything else available.
They have developed an artificial visual system known as the Cellular Neural Network Universal Machine, which can mimic all the functions of the human eye, from motion detection to color processing.
It is well known that shunting inhibitory cellular neural networks (SICNNs) [1] have many applications in psychophysics, speech, perception, robotics, adaptive pattern recognition, vision, and image processing.
Ultra Low Noise Signed Digit Arithmetic Using Cellular Neural Networks // Proceedings of the 4th IEEE International Workshop on System-on-Chip for Real-Time Applications.
Neural networks such as Hopfield neural networks (HNNs), cellular neural networks (CNNs), and Cohen-Grossberg neural networks (CGNNs), with time delays, have been extensively studied in past years due to their many applications in different areas such as pattern recognition, associative memory, and combinatorial optimization, see [1- 10].
Universality and emergent computation in cellular neural networks.
Cellular neural networks were introduced in 1988 [1] as an alternative to the usual artificial neural networks.

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