The rapid development of deep learning raises a massive demand for computing power. However, traditional siliconbased chips based on the von Neumann architecture with physically separated memory and computing units, are facing critical issues such as the "memory wall", and hence the increase of chip computing power is gradually hitting a bottleneck. To address this problem, researchers have been inspired by the working mechanism of biological brain and proposed a computing-inmemory architecture based on memristors. This novel architecture is expected to achieve several orders of magnitude improvement in energy efficiency and speed over the von Neumann architecture for tasks such as artificial neural networks. It is one of the most promising technologies to achieve ultra-low power consumption and ultra-high computing power. This article first reviews the working mechanisms of various types of memristors, and summarizes the latest device research internationally. Then, the progress on application demonstrations of memristor-based computing-in-memory chips such as neural networks, signal processing, and machine learning are reviewed. The current challenges in this field and further research directions are concluded in the end.

memristor  /  brain-inspired computing  /  computing-in-memory  /  neural networks  /  signal processing