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Article: Memristor-based signal processing for edge computing

TitleMemristor-based signal processing for edge computing
Authors
Keywordsedge computing
in-memory computing
Internet of Things (IoTs)
memristor
signal processing
Issue Date2022
Citation
Tsinghua Science and Technology, 2022, v. 27, n. 3, p. 455-471 How to Cite?
AbstractThe 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.
Persistent Identifierhttp://hdl.handle.net/10722/334795
ISSN
2023 Impact Factor: 5.2
2023 SCImago Journal Rankings: 1.580
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhao, Han-
dc.contributor.authorLiu, Zhengwu-
dc.contributor.authorTang, Jianshi-
dc.contributor.authorGao, Bin-
dc.contributor.authorZhang, Yufeng-
dc.contributor.authorQian, He-
dc.contributor.authorWu, Huaqiang-
dc.date.accessioned2023-10-20T06:50:48Z-
dc.date.available2023-10-20T06:50:48Z-
dc.date.issued2022-
dc.identifier.citationTsinghua Science and Technology, 2022, v. 27, n. 3, p. 455-471-
dc.identifier.issn1007-0214-
dc.identifier.urihttp://hdl.handle.net/10722/334795-
dc.description.abstractThe 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.-
dc.languageeng-
dc.relation.ispartofTsinghua Science and Technology-
dc.subjectedge computing-
dc.subjectin-memory computing-
dc.subjectInternet of Things (IoTs)-
dc.subjectmemristor-
dc.subjectsignal processing-
dc.titleMemristor-based signal processing for edge computing-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.26599/TST.2021.9010043-
dc.identifier.scopuseid_2-s2.0-85119498541-
dc.identifier.volume27-
dc.identifier.issue3-
dc.identifier.spage455-
dc.identifier.epage471-
dc.identifier.eissn1878-7606-
dc.identifier.isiWOS:000718022200004-

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