As data-intensive applications increasingly strain conventional computing systems, processing-in-memory (PIM) has emerged as a promising paradigm to alleviate the memory wall by minimizing data transfer between memory and processing units. This review presents the recent advances in both stateful and non-stateful logic techniques for PIM, focusing on emerging nonvolatile memory technologies such as resistive random-access memory (RRAM), phase-change memory (PCM), and magnetoresistive random-access memory (MRAM). Both experimentally demonstrated and simulated logic designs are critically examined, highlighting key challenges in reliability and the role of device-level optimization in enabling scalable and commercial viable PIM systems. The review begins with an overview of relevant logic families, memristive device types, and associated reliability metrics. Each logic family is then explored in terms of how it capitalizes on distinct device properties to implement logic techniques. A comparative table of representative device stacks and performance parameters illustrates trade-offs and quality indicators. Through this comprehensive analysis, the development of optimized, robust memristive devices for next-generation PIM applications is supported.
Continuous-Time Digital Twin with Analogue Memristive Neural Ordinary Differential Equation Solver
the University of Hong Kong（香港大学）；ACCESS – AI Chip Center for Emerging Smart Systems, InnoHK Centers（ACCESS——新兴智能系统AI芯片中心（InnoHK中心））；Institute of the Mind, the University of Hong Kong（香港大学心智研究院）
Dynamic neural network with memristive CIM and CAM for 2D and 3D vision
the University of Hong Kong（香港大学）；ACCESS – AI Chip Center for Emerging Smart Systems, InnoHK Centers（ACCESS——新兴智能系统AI芯片中心（InnoHK中心））；Institute of the Mind, the University of Hong Kong（香港大学心智研究所）；Institute of Microelectronics, Chinese Academy of Sciences（中国科学院微电子研究所）；University of Chinese Academy of Sciences（中国科学院大学）
