Fading memory effects in a memristor for Cellular Nanoscale Network applications

CNN based analogic cellular computing is a unified paradigm for universal spatio-temporal computation with several applications in a large number of different fields of research. By endowing CNN with local memory, control, and communication circuitry, many different hardware architectures with stored programmability, showing an enormous computing power - trillion of operations per second may be executed on a single chip -, have been realized. The complex spatio-temporal dynamics emerging in certain CNN may lead to the development of more efficient information processing methods as compared to conventional strategies. Memristors exhibit a rich variety of nonlinear behaviours, occupy a negligible amount of integrated circuit area, consume very little power, are suited to a massively-parallel data flow, and may combine data storage with signal processing. As a result, the use of memristors in future CNN-based computing structures may improve and/or extend the functionalities of state-of-the art hardware architectures. This contribution provides a detailed analysis of the system-theoretic model of a tantalum oxide memristor, in view of its potential adoption for the implementation of synaptic operators in CNN architectures.