Abstract
The cellular neural network (CNN) architecture combines the best features from traditional fully-connected analog neural networks and digital cellular automata. The network can rapidly process continuous-valued (grayscale) input signals (such as images) and perform many computation functions which traditionally were implemented in digital form. Here, we briefly introduce the the theory of CNN circuits, provide some examples of CNN applications to image processing, and discuss work toward a CNN implementation in custom CMOS VLSI. The role of analog computer-aided design (CAD) will be briefly presented as it relates to analog neural network implementation.
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Chua, L.O., Yang, L., Krieg, K.R. (1991). Signal Processing Using Cellular Neural Networks. In: Nossek, J.A. (eds) Parallel Processing on VLSI Arrays. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4036-6_3
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DOI: https://doi.org/10.1007/978-1-4615-4036-6_3
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