存算一体技术研究现状OA北大核心CSTPCD

Von Neumann computer architecture faces the bottleneck of"storage wall",which hindering the performance improvement of AI(Artificial Intelligence)computing.Computing-In-Memory(CIM)breaks the limitation of"storage wall"and greatly improves the performance of AI computing.At present,CIM schemes have been implemented in a variety of storage media.According to the type of calculation signal,CIM scheme can be divided into digital CIM and analog CIM scheme.CIM has greatly improved the performance of AI computing,but the further development still faces major challenges.This article provides a detailed comparative analysis of CIM schemes in different signal domains,pointing out the main advantages and disadvantages of each scheme,and also pointing out the challenges faced by CIM.We believe that with the cross level col-laborative research and development of process integration,devices,circuits,architecture,and software toolchains,CIM will provide more powerful and efficient computing power for AI computing at the edge and cloud ends.