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Article: In-Memory Computing with Memristor Content Addressable Memories for Pattern Matching

TitleIn-Memory Computing with Memristor Content Addressable Memories for Pattern Matching
Authors
Keywordsfinite state machines
memristors
content addressable memory
in-memory computing
Issue Date2020
Citation
Advanced Materials, 2020, v. 32 n. 37, article no. 2003437 How to Cite?
Abstract© 2020 Wiley-VCH GmbH. The dramatic rise of data-intensive workloads has revived application-specific computational hardware for continuing speed and power improvements, frequently achieved by limiting data movement and implementing “in-memory computation”. However, conventional complementary metal oxide semiconductor (CMOS) circuit designs can still suffer low power efficiency, motivating designs leveraging nonvolatile resistive random access memory (ReRAM), and with many studies focusing on crossbar circuit architectures. Another circuit primitive—content addressable memory (CAM)—shows great promise for mapping a diverse range of computational models for in-memory computation, with recent ReRAM–CAM designs proposed but few experimentally demonstrated. Here, programming and control of memristors across an 86 × 12 memristor ternary CAM (TCAM) array integrated with CMOS are demonstrated, and parameter tradeoffs for optimizing speed and search margin are evaluated. In addition to smaller area, this memristor TCAM results in significantly lower power due to very low programmable conductance states, motivating CAM use in a wider range of computational applications than conventional TCAMs are confined to today. Finally, the first experimental demonstration of two computational models in memristor TCAM arrays is reported: regular expression matching in a finite state machine for network security intrusion detection and definable inexact pattern matching in a Levenshtein automata for genomic sequencing.
Persistent Identifierhttp://hdl.handle.net/10722/286814
ISSN
2023 Impact Factor: 27.4
2023 SCImago Journal Rankings: 9.191
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGraves, Catherine E.-
dc.contributor.authorLi, Can-
dc.contributor.authorSheng, Xia-
dc.contributor.authorMiller, Darrin-
dc.contributor.authorIgnowski, Jim-
dc.contributor.authorKiyama, Lennie-
dc.contributor.authorStrachan, John Paul-
dc.date.accessioned2020-09-07T11:45:44Z-
dc.date.available2020-09-07T11:45:44Z-
dc.date.issued2020-
dc.identifier.citationAdvanced Materials, 2020, v. 32 n. 37, article no. 2003437-
dc.identifier.issn0935-9648-
dc.identifier.urihttp://hdl.handle.net/10722/286814-
dc.description.abstract© 2020 Wiley-VCH GmbH. The dramatic rise of data-intensive workloads has revived application-specific computational hardware for continuing speed and power improvements, frequently achieved by limiting data movement and implementing “in-memory computation”. However, conventional complementary metal oxide semiconductor (CMOS) circuit designs can still suffer low power efficiency, motivating designs leveraging nonvolatile resistive random access memory (ReRAM), and with many studies focusing on crossbar circuit architectures. Another circuit primitive—content addressable memory (CAM)—shows great promise for mapping a diverse range of computational models for in-memory computation, with recent ReRAM–CAM designs proposed but few experimentally demonstrated. Here, programming and control of memristors across an 86 × 12 memristor ternary CAM (TCAM) array integrated with CMOS are demonstrated, and parameter tradeoffs for optimizing speed and search margin are evaluated. In addition to smaller area, this memristor TCAM results in significantly lower power due to very low programmable conductance states, motivating CAM use in a wider range of computational applications than conventional TCAMs are confined to today. Finally, the first experimental demonstration of two computational models in memristor TCAM arrays is reported: regular expression matching in a finite state machine for network security intrusion detection and definable inexact pattern matching in a Levenshtein automata for genomic sequencing.-
dc.languageeng-
dc.relation.ispartofAdvanced Materials-
dc.subjectfinite state machines-
dc.subjectmemristors-
dc.subjectcontent addressable memory-
dc.subjectin-memory computing-
dc.titleIn-Memory Computing with Memristor Content Addressable Memories for Pattern Matching-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/adma.202003437-
dc.identifier.pmid32761709-
dc.identifier.scopuseid_2-s2.0-85088992335-
dc.identifier.volume32-
dc.identifier.issue37-
dc.identifier.spagearticle no. 2003437-
dc.identifier.epagearticle no. 2003437-
dc.identifier.eissn1521-4095-
dc.identifier.isiWOS:000555849300001-
dc.identifier.issnl0935-9648-

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