Implementation and Improvement of Dynamic Logic Gates Based on Cellular Neural Networks

This Paper explores using a non-linear system to construct dynamic logic architecture-cellular neural networks (CNN). The proposed CNN schemes can discriminate the two input signals and switch easily among different 16 kinds of operational roles by changing parameters. Each logic cell performs more flexibly, that makes it possible to achieve complex logic operations and construct computing architecture with less logic cells. We also proposed a new formula of hysteresis CNN to ensure that the output is strict binary.