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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. Key words: Cellular neural networks, simulators, non-linear circuits, transputer, parallel processing.

In this paper a pipelined ring algorithm is presented for efficient computation of one and two dimensional Fast Fourier Transform (FFT) on a message passing multiprocessor. The algorithm has been implemented on a transputer based system and experiments reveal that the algorithm is very efficient. A model for analysing the performance of the algorithm is developed from its computation-communication characteristics. Expressions for execution time, speedup and efficiency are obtained and these expressions are validated with experimental results obtained on a four transputer system. The analytical model is then used to estimate the performance of the algorithm for different number of processors, and for different sizes of the input data.