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Tsinghua Science and Technology

Authors

Han Zhao, School of Integrated Circuits, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China;Department of Microelectronics Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Zhengwu Liu, School of Integrated Circuits, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
Jianshi Tang, School of Integrated Circuits, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China;Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China
Bin Gao, School of Integrated Circuits, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China;Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China
Yufeng Zhang, Department of Microelectronics Science and Technology, Harbin Institute of Technology, Harbin 150001, China
He Qian, School of Integrated Circuits, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China;Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China
Huaqiang Wu, School of Integrated Circuits, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China;Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China

Keywords

memristor, signal processing, edge computing, Internet of Things (IoTs), in-memory computing

Abstract

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.

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