A Flexible Memristor-Based Neural Network

Abstract

Many memristor-based neural network arrays that have been proposed in recent years are simultaneously dealt with all of their signal inputs in signal reception status. Therefore, when a relatively small-scale neural network is implemented with this memristive array, some of the inputs which are not used may cause errors in the result due to the impact of an unexpected signal. In this paper, a flexible memristor-based neural network is proposed. Based on this network, the number of synapses used at work can be flexibly configured according to the required size, thereby improving system performance. The memristor-based neural network is simulated in Pspice to implement two different scales, which proves the feasibility and effectiveness of a flexible memristive neural network.