An artificial intelligence (AI) processor is a promising solution for energy-efficient data processing, including health monitoring and image/voice recognition. However, data movements between compute part and memory induce memory wall and power wall challenges to the conventional computing architecture. Recently, the memory-centric architecture has been revised to solve the data movement issue, where the memory is equipped with the compute-capable memory technique, namely, computing-in-memory (CIM). In this paper, we analyze the requirement of AI algorithms on the data movement and low power requirement of AI processors. In addition, we introduce the story of CIM and implementation methodologies of CIM architecture. Furthermore, we present several novel solutions beyond traditional analog-digital mixed static random-access memory (SRAM)-based CIM architecture. Finally, recent CIM tape-out studies are listed and discussed.

人工智能（AI）处理器是用于节能数据处理（包括健康监控和图像/语音识别）的有前途的解决方案。但是，计算部分和内存之间的数据移动给常规计算体系结构带来了内存壁和功耗壁的挑战。近来，以存储器为中心的体系结构已被修改以解决数据移动问题，其中存储器配备有具有计算能力的存储技术，即，内存中计算（CIM）。在本文中，我们分析了AI算法对AI处理器的数据移动和低功耗要求的要求。另外，我们介绍了CIM的故事以及CIM体系结构的实现方法。此外，除了基于传统的模拟-数字混合静态随机存取存储器（SRAM）的CIM体系结构，我们还提供了几种新颖的解决方案。最后，列出并讨论了最新的CIM流片研究。