Artificial intelligence (AI) processes data-centric applications with minimal effort. However, it poses new challenges to system design in terms of computational speed and energy efficiency. The traditional von Neumann architecture cannot meet the requirements of heavily data-centric applications due to the separation of computation and storage. The emergence of computing in-memory (CIM) is significant in circumventing the von Neumann bottleneck. A commercialized memory architecture, static random-access memory (SRAM), is fast and robust, consumes less power, and is compatible with state-of-the-art technology. This study investigates the research progress of SRAM-based CIM technology in three levels: circuit, function, and application. It also outlines the problems, challenges, and prospects of SRAM-based CIM macros.

中文翻译：

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基于 SRAM 的内存计算综述：电路、功能和应用

人工智能 (AI) 可以轻松处理以数据为中心的应用程序。然而，它在计算速度和能效方面对系统设计提出了新的挑战。由于计算和存储分离，传统的冯诺依曼架构无法满足以数据为中心的应用程序的需求。内存计算（CIM）的出现对于规避冯诺依曼瓶颈具有重要意义。一种商业化的内存架构，即静态随机存取内存 (SRAM)，速度快且稳健，功耗更低，并且与最先进的技术兼容。本研究从电路、功能和应用三个层面探讨了基于SRAM的CIM技术的研究进展。它还概述了基于 SRAM 的 CIM 宏的问题、挑战和前景。