Memristor-based spiking neural networks: cooperative development of neural network architecture/algorithms and memristors

Abstract

Inspired by the structure and principles of the human brain, spike neural networks (SNNs) appear as the latest generation of artificial neural networks, attracting significant and universal attention due to their remarkable low-energy transmission by pulse and powerful capability for large-scale parallel computation. Current researches on artificial neural networks gradually change from software simulation into hardware implementation. However, such a process is fraught with challenges. In particular, memristors are highly anticipated hardware candidate considering of their fast-programming speed, low power consumption, and compatibility with the CMOS technology. In this review, we start from the basic principles of SNNs, and then introduce memristor-based technologies for hardware implementation of SNNs, and further discuss the feasibility of integrating customized algorithm optimization to promote efficient and energy-saving SNN hardware systems. Finally, based on existing memristor technology, we summarize the current problems and challenges in this field.