Memristive Learning Cellular Automata: Theory and Applications

Memristors are novel non volatile devices that manage to combine storing and processing capabilities in the same physical place. Their nanoscale dimensions and low power consumption enable the further design of various nanoelectronic processing circuits and corresponding computing architectures, like neuromorphic, in memory, unconventional, etc. One of the possible ways to exploit the memristor's advantages is by combining them with Cellular Automata (CA). CA constitute a well known non von Neumann computing architecture that is based on the local interconnection of simple identical cells forming N-dimensional grids. These local interconnections allow the emergence of global and complex phenomena. In this paper, we propose a hybridization of the CA original definition coupled with memristor based implementation, and, more specifically, we focus on Memristive Learning Cellular Automata (MLCA), which have the ability of learning using also simple identical interconnected cells and taking advantage of the memristor devices inherent variability. The proposed MLCA circuit level implementation is applied on optimal detection of edges in image processing through a series of SPICE simulations, proving its robustness and efficacy.