In-memory computing based on phase change memory for high energy efficiency

被引：0
|
作者：
Luchang HE [1 ,2 ]
Xi LI [1 ]
Chenchen XIE [1 ]
Zhitang SONG [1 ]
机构：
[1] National Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Micro-System and Information Technology, Chinese Academy of Sciences
[2] School of Microelectronics, University of Science and Technology of China
来源：
Science China(Information Sciences) | 2023年 / 66卷 / 10期
基金：
中国博士后科学基金; 国家重点研发计划; 中国国家自然科学基金;
关键词：
D O I：
暂无
中图分类号：
TP333 [存贮器];
学科分类号：
081201 ;
摘要：
The energy efficiency issue caused by the memory wall in traditional von Neumann architecture is difficult to reconcile. In-memory computing(CIM) based on emerging nonvolatile memory(NVM) is a promising solution to avoid data movement between storage and processors and realize highly energy-efficient computing. Compared with other NVM technologies, phase change random access memory(PCM) exhibits comprehensive performance for analog computing. In this paper, we review advanced PCM techniques,including phase-change materials, mechanisms, and unique properties, as a foundation and inspiration for implementing CIM architecture. Meanwhile, state-of-the-art PCM-based CIM systems are well discussed for high energy efficiency in artificial neural networks, spiking neural networks, and other artificial intelligence(AI) applications. Finally, we present the remaining challenges and potential solutions of CIM for further investigation.
引用
收藏
页码：20 / 41
页数：22
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