A novel memristor-based method to compute eigenpairs

Abstract

Although digital processors offer high computing accuracy, they suffer enormously from lengthy execution times and high energy consumption as a result of the numerous communications between the processors and storage units. The disadvantage is especially acute when performing data-intensive operations, such as deep neural networks and matrix operations. To address this, several novel ideas and devices for implementing in-memory computing have been proposed. One of them is the memristor. Because of their scalability, nonvolatility, and analog storage characteristics, memristors have considerable potential and have achieved some encouraging research results. An eigenpair estimation method and a memristor-based crossbar structure are presented in this paper. The method differs from conventional computers in that the execution is carried out with the least number of controls and data transfers as possible. Almost all of the desired outcomes can be attributed to fundamental physical laws, such as Ohm’s law and Kirchhoff’s law. This method is then applied to principal component analysis (PCA) in the end.