Spatially distributed cellular neural networks

Abstract

PurposeThe purpose of this paper is to develop a methodology for the design of cellular neural networks with interconnection topologies optimized and suitable for spatially distributed implementation.Design/methodology/approachThe authors perform combinatorial optimization on the neural network's topology to obtain a sparser network, in which the links between the components of the network that reside in different physical locations are minimized. The approach builds on existing computationally efficient tools for the design of cellular neural networks and uses the concept of the network's stability parameters to assess the performance of the network prior to testing.FindingsIt turns out that the sparser cellular neural networks thus produced exhibit performance that can be on par with that of networks with full connectivity, and that for implementations of modest size, communication delays are not that significant to affect the stability of the dynamical system.Originality/valueThe novelty of the proposed approach lies in the formulation of the combinatorial optimization problem in a way that trades‐off network performance for communication overhead, and the use of this method for the physical implementation of associative memories across different interconnected processors.