An overview on memristor crossabr based neuromorphic circuit and architecture

As technology advances, artificial intelligence becomes pervasive in society and ubiquitous in our lives, which stimulates the desire for embedded-everywhere and human-centric intelligent computation paradigm. However, conventional instruction-based computer architecture was designed for algorithmic and exact calculations. It is not suitable for handling the applications of machine learning and neural networks that usually involve a large sets of noisy and incomplete natural data. Instead, neuromorphic systems inspired by the working mechanism of human brains create promising potential. Neuromorphic systems possess a massively parallel architecture with closely coupled memory and computing. Moreover, through the sparse utilizations of hardware resources in time and space, extremely high power efficiency can be achieved. In recent years, the use of memristor technology in neuromorphic systems has attracted growing attention for its distinctive properties, such as nonvolatility, reconfigurability, and analog processing capability. In this paper, we summarize the research efforts in the development of memristor crossbar based neuromorphic design from the perspectives of device modeling, circuit, architecture, and design automation.