Contents I. Introduction
With the rapid development of artificial intelligence (AI) algorithm, the research and application of convolutional neural network (CNN) has become more and more extensive. However, in conventional Von-Neumann architecture, memories and computing units are connected with limited bus. The frequent transfer of data between memories and computing units will generate huge energy consumption. This severely limits the development of convolutional neural networks, which has large amounts of data and high computational density [1]. In order to overcome the above limitations, the architecture of Computation-in-Memory (CIM) was proposed and became a promising field both in the academia and industry [2]-[6]. CIM architecture embeds the computing circuits in the memory, therefore, it can perform some calculations in the memory while serving as an ordinary memory. Computation in memory will greatly reduce the data migration, the energy consumption of accessing memory and increases the calculation speed [2]-[14]. And CIM architecture is considered to be one of the mainstream trends of artificial intelligence algorithm hardware acceleration in the future.
