Hauptseite > Publikationsdatenbank > Dynamical memristors for higher-complexity neuromorphic computing > print

001     910701
005     20240403082801.0
024 7 _ |a 10.1038/s41578-022-00434-z
|2 doi
024 7 _ |a WOS:000783161600001
|2 WOS
037 _ _ |a FZJ-2022-04073
082 _ _ |a 600
100 1 _ |a Kumar, Suhas
|0 0000-0002-6772-7250
|b 0
245 _ _ |a Dynamical memristors for higher-complexity neuromorphic computing
260 _ _ |a Basingstoke
|c 2022
|b Nature Publishing Group
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1710328456_3659
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Research on electronic devices and materials is currently driven by both the slowing down of transistor scaling and the exponential growth of computing needs, which make present digital computing increasingly capacity-limited and power-limited. A promising alternative approach consists in performing computing based on intrinsic device dynamics, such that each device functionally replaces elaborate digital circuits, leading to adaptive ‘complex computing’. Memristors are a class of devices that naturally embody higher-order dynamics through their internal electrophysical processes. In this Review, we discuss how novel material properties enable complex dynamics and define different orders of complexity in memristor devices and systems. These native complex dynamics at the device level enable new computing architectures, such as brain-inspired neuromorphic systems, which offer both high energy efficiency and high computing capacity.
536 _ _ |a 5234 - Emerging NC Architectures (POF4-523)
|0 G:(DE-HGF)POF4-5234
|c POF4-523
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Wang, Xinxin
|b 1
700 1 _ |a Strachan, John Paul
|b 2
700 1 _ |a Yang, Yuchao
|0 0000-0003-4674-4059
|b 3
700 1 _ |a Lu, Wei D.
|0 0000-0003-4731-1976
|b 4
773 _ _ |a 10.1038/s41578-022-00434-z
|g Vol. 7, no. 7, p. 575 - 591
|0 PERI:(DE-600)2844635-5
|n 7
|p 575 - 591
|t Nature reviews
|v 7
|y 2022
|x 2058-8437
909 C O |o oai:juser.fz-juelich.de:910701
|p VDB
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-523
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Neuromorphic Computing and Network Dynamics
|9 G:(DE-HGF)POF4-5234
|x 0
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NAT REV MATER : 2019
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2021-02-02
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-02-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2021-02-02
915 _ _ |a IF >= 70
|0 StatID:(DE-HGF)9970
|2 StatID
|b NAT REV MATER : 2019
|d 2021-02-02
920 1 _ |0 I:(DE-Juel1)PGI-14-20210412
|k PGI-14
|l Neuromorphic Compute Nodes
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)PGI-14-20210412
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21