On Learning with Nonlinear Memristor-Based Neural Network and its Replication

Abstract

Nonlinear memristor-based neural network and a circuit-based learning system is addressed in this work. The weights of the neural network are based on the memristor bridge synapse and the learning architecture is designed in analog-digital mixed circuits by adopting a simple learning algorithm called random weight change algorithm. Though the memristor bridge can be efficiently used as a synapse, it still suffers from nonlinearity in weight programming at its extremes due to the boundary effect of memristors, which is a common phenomenon in most of the nano-devices. In this study, a novel architecture of a modified memristor bridge synapse is proposed that avoids the boundary effect issue by shifting the programming origin to the middle of the linear region. To demonstrate the effectiveness of the proposed method, multilayer neural network along with the end-to-end learning architecture is designed in circuit with nonlinear memristors and tested for several classical learning problems. Simulation results showing successful learning and correct behavior of replicated neural network are presented.