In today's data-centric world, where data fuels numerous application domains, with machine learning at the forefront, handling the enormous volume of data efficiently in terms of time and energy presents a formidable challenge. Conventional computing systems and accelerators are continually being pushed to their limits to stay competitive. In this context, computing near-memory (CNM) and computing-in-memory (CIM) have emerged as potentially game-changing paradigms. This survey introduces the basics of CNM and CIM architectures, including their underlying technologies and working principles. We focus particularly on CIM and CNM architectures that have either been prototyped or commercialized. While surveying the evolving CIM and CNM landscape in academia and industry, we discuss the potential benefits in terms of performance, energy, and cost, along with the challenges associated with these cutting-edge computing paradigms.

翻译：在当今以数据为中心的世界中，数据驱动着包括机器学习在内的众多应用领域，如何高效地处理海量数据（同时兼顾时间与能耗）成为严峻挑战。传统计算系统与加速器正不断被推向性能极限。在此背景下，近存计算（CNM）与存内计算（CIM）作为可能颠覆传统范式的技术应运而生。本综述介绍了CNM与CIM架构的基础知识，包括其底层技术及工作原理。我们重点关注已实现原型或商业化的CIM与CNM架构。在梳理学术界与工业界CIM及CNM演进格局的同时，本文讨论了这些前沿计算范式在性能、能耗与成本方面的潜在优势，以及所面临的挑战。