Cellular neural networks as a general massively parallel computational paradigm

Abstract

In this paper is presented the use of the discrete-time cellular neural network (DTCNN) paradigm to develop algorithms devised for general-purpose massively parallel processing (MPP) systems. This paradigm is defined in discrete N-dimensional spaces (lattices) and is characterized by the locality of the direct information transmission between the space points (cells) and by continuous values of data and parameters; the DTCNN paradigm is thus able to express most of the typical MPP applications. A general version of a DTCNN has been implemented and optimized for three MPP architectures, namely the Connection Machines CM-2 and CM-5 and the Cray T3D. The comparison between the three machine performances with those achieved by a standard SPARC-20 workstation shows that, particularly with large lattices, the speed-up allowed in the computational times is significant and the range of solvable problem sizes is widely extended.