Abstract: In the process of processing data-intensive applications such as deep neural networks, the frequent transfer of large amounts of data between the processor and the memory causes severe performance loss and energy consumption, which is the biggest bottleneck of the current von Neumann architecture. In view of the limitations of the traditional von Neumann architecture, the SRAM-based in-memory computing technology integrates the computing unit into the memory to support data storage and calculation, which completely breaks through the von Neumann bottleneck and is expected to become a new generation Intelligent computing architecture.This paper clarifies the problems of "power wall" and "storage wall" caused by the von Neumann architecture from the perspective of architecture, and gives the reasons for the rise of in-memory computing. The paper focuses on the research of SRAM-based in-memory computing architectures in recent years, and describes the working mechanism, advantages and disadvantages and significance of various SRAM-based in-memory computing architectures by taking several classical architectures as examples. And from the perspective of device level, circuit level and architecture level, the key factors of current SRAM-based in-memory computing technology are summarized respectively. The SRAM-based in-memory computing technology is a promising and versatile technology that will provide efficient and low-energy system architectures for machine learning applications, graph computing applications and genetic engineering. The paper looks forward to the development of SRAM-based in-memory computing technology in devices, circuits and architectures in the coming year