001     912510
005     20230224084246.0
024 7 _ |a 10.1109/JXCDC.2022.3217098
|2 doi
024 7 _ |a 2128/33019
|2 Handle
024 7 _ |a WOS:000915312400008
|2 WOS
037 _ _ |a FZJ-2022-05683
082 _ _ |a 530
100 1 _ |a Freye, Florian
|0 0000-0003-3025-8910
|b 0
245 _ _ |a Memristive Devices for Time Domain Compute-in-Memory
260 _ _ |a New York, NY
|c 2022
|b IEEE
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 1670913545_31169
|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 Analog compute schemes and compute-in-memory (CIM) have emerged in an effort to reduce the increasing power hunger of convolutional neural networks (CNNs), which exceeds the constraints of edge devices. Memristive device types are a relatively new offering with interesting opportunities for unexplored circuit concepts. In this work, the use of memristive devices in cascaded time-domain CIM (TDCIM) is introduced with the primary goal of reducing the size of fully unrolled architectures. The different effects influencing the determinism in memristive devices are outlined together with reliability concerns. Architectures for binary as well as multibit multiply and accumulate (MAC) cells are presented and evaluated. As more involved circuits offer more accurate compute result, a tradeoff between design effort and accuracy comes into the picture. To further evaluate this tradeoff, the impact of variations on overall compute accuracy is discussed. The presented cells reach an energy/OP of 0.23 fJ at a size of 1.2 μm2 for binary and 6.04 fJ at 3.2 μm2 for 4×4 bit MAC operations.
536 _ _ |a 5233 - Memristive Materials and Devices (POF4-523)
|0 G:(DE-HGF)POF4-5233
|c POF4-523
|f POF IV
|x 0
536 _ _ |a BMBF-16ME0399 - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0399)
|0 G:(DE-82)BMBF-16ME0399
|c BMBF-16ME0399
|x 1
536 _ _ |a BMBF-16ME0398K - Verbundprojekt: Neuro-inspirierte Technologien der künstlichen Intelligenz für die Elektronik der Zukunft - NEUROTEC II - (BMBF-16ME0398K)
|0 G:(DE-82)BMBF-16ME0398K
|c BMBF-16ME0398K
|x 2
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Lou, Jie
|0 0000-0003-0380-8585
|b 1
700 1 _ |a Bengel, Christopher
|b 2
700 1 _ |a Menzel, Stephan
|0 P:(DE-Juel1)158062
|b 3
700 1 _ |a Wiefels, Stefan
|0 P:(DE-Juel1)187229
|b 4
700 1 _ |a Gemmeke, Tobias
|0 0000-0003-1583-3411
|b 5
773 _ _ |a 10.1109/JXCDC.2022.3217098
|g Vol. 8, no. 2, p. 119 - 127
|0 PERI:(DE-600)2840841-X
|n 2
|p 119 - 127
|t IEEE journal on exploratory solid-state computational devices and circuits
|v 8
|y 2022
|x 2329-9231
856 4 _ |u https://juser.fz-juelich.de/record/912510/files/Freye2022001.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:912510
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)158062
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)187229
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)POF4-5233
|x 0
914 1 _ |y 2022
915 _ _ |a Creative Commons Attribution CC BY 4.0
|0 LIC:(DE-HGF)CCBY4
|2 HGFVOC
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2020-08-26
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2020-08-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2021-01-15T11:05:14Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2021-01-15T11:05:14Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Blind peer review
|d 2021-01-15T11:05:14Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-23
915 _ _ |a WoS
|0 StatID:(DE-HGF)0112
|2 StatID
|b Emerging Sources Citation Index
|d 2022-11-23
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-23
920 1 _ |0 I:(DE-Juel1)PGI-7-20110106
|k PGI-7
|l Elektronische Materialien
|x 0
920 1 _ |0 I:(DE-82)080009_20140620
|k JARA-FIT
|l JARA-FIT
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)PGI-7-20110106
980 _ _ |a I:(DE-82)080009_20140620
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21