Memristor-Based Signal Processing for Edge Computing  

作　　者：Han Zhao Zhengwu Liu Jianshi Tang Bin Gao Yufeng Zhang He Qian Huaqiang Wu 

机构地区：[1]the School of Integrated Circuits,Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing 100084,China [2]the Department of Microelectronics Science and Technology,Harbin Institute of Technology,Harbin 150001,China [3]Beijing Innovation Center for Future Chips,Tsinghua University,Beijing 100084,China

出　　处：《Tsinghua Science and Technology》2022年第3期455-471,共17页清华大学学报（自然科学版（英文版）

基　　金：supported in part by the National Science and Technology Major Project of China(No.2017ZX02315001-005);the National Natural Science Foundation of China(Nos.91964104 and 61974081)。

摘　　要：The rapid growth of the Internet of Things(IoTs)has resulted in an explosive increase in data,and thus has raised new challenges for data processing units.Edge computing,which settles signal processing and computing tasks at the edge of networks rather than uploading data to the cloud,can reduce the amount of data for transmission and is a promising solution to address the challenges.One of the potential candidates for edge computing is a memristor,an emerging nonvolatile memory device that has the capability of in-memory computing.In this article,from the perspective of edge computing,we review recent progress on memristor-based signal processing methods,especially on the aspects of signal preprocessing and feature extraction.Then,we describe memristor-based signal classification and regression,and end-to-end signal processing.In all these applications,memristors serve as critical accelerators to greatly improve the overall system performance,such as power efficiency and processing speed.Finally,we discuss existing challenges and future outlooks for memristor-based signal processing systems.

关 键 词：MEMRISTOR signal processing edge computing Internet of Things(IoTs) in-memory computing 

分 类 号：TN60[电子电信—电路与系统] TN911.7