A very fast CMOS artificial cellular neural network

In this paper, techniques for the design of fast artificial cellular neural networks are presented. The speed limitations of the artificial Cellular Neural Networks (CNN´s) are further discussed. As a result of this study an efficient and high speed CMOS architecture is proposed. In the implementation of the building blocks, very much attention is paid to speed, power consumption and silicon area. The convergence time of the network can easily be as short as 200 nano-seconds. The current consumption of a single neuron is less than 100 μA, if all the templates are active. The network is internally re-configurable for noise reduction and edge detection. Simulated results for a 10×10 network, consisting of 1700 transistors, configured to remove noise are reported. The supply voltages are only 0-3 volts