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Open Access

Memristor-Based Signal Processing for Edge Computing

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
Beijing Innovation Center for Future Chips, Tsinghua University, Beijing 100084, China
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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.

Keywords

memristorsignal processingedge computingInternet of Things (IoTs)in-memory computing

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Tsinghua Science and Technology
Volume 27 Issue 3,
June 2022
Pages 455-471
DOI: 10.26599/TST.2021.9010043
Cite this article:
Zhao H, Liu Z, Tang J, et al. Memristor-Based Signal Processing for Edge Computing. Tsinghua Science and Technology, 2022, 27(3): 455-471. https://doi.org/10.26599/TST.2021.9010043

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Received: 27 February 2021
Revised: 15 June 2021
Accepted: 27 June 2021
Published: 13 November 2021
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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