Parallel simulation of cellular neural networks

Abstract

In this paper a new simulator for cellular neural networks (CNN) called PSIMCNN is presented. It has been studied for a parallel general-purpose computing architecture based on transputers. The Gauss-Jacobi waveform relaxation (WR) algorithm has been adopted. It has been analytically proved that the WR algorithm is convergent for the most common CNN models. Implementation issues have been described and some experimental results comprising classical CNN and Chua circuits CNN simulations have been presented. Parallelism and performances have been investigated. Moreover, a comparison with the CNN-HAC simulator is reported.