FPGA-based generation of autowaves in Memristive Cellular Neural Networks

Cellular Neural/Nonlinear Networks (CNNs) constitute an effective approach for studying complex phenomena like autowaves, spiral waves or pattern formation either by providing a computationally efficient environment for numerical simulations or by allowing the possibility of hardware emulators of the system under study. In this work, we focus on a CNN made of memristor-based cells, namely a Memristive Cellular Neural/Nonlinear Network (MCNN). This has been recently shown to be capable of generating complex phenomena such as autowave propagation. In this work, we implement such a MCNN by using Field Programmable Gate Array (FPGA). Our system consisting of a FPGA development board connected to a monitor allows us to emulate autowave propagation in an efficient way. Experimental results show the feasibility of FPGA-based approach to implement MCNN. � 2012 IEEE.