VLSI Design of Digital Cellular Neural Networks for Image Processing

Abstract

Biologically inspired analog VLSI systems have attracted more and more studies. For system consistency and for quick application of neural systems, we have established digital models for neuron operations such that the cell operations can be implemented with simple digital adders and multipliers. To provide fast data transfer and minimum reconfiguration for various sizes of networks, the architecture is designed for low hardware complexity and high mask flexibility, and is perfectly suited for VLSI array implementation. The parallel computation model derived here is used to realize parallel array processor architectures for digital neural networks. Some image processing operations have been simulated. The tolerable error rate compared to the analog model and the competent speed performance make this applicable to digital systems and suitable for 2-D signal processing.