001     1028664
005     20250220092006.0
024 7 _ |a 10.1109/MetroXRAINE58569.2023.10405732
|2 doi
024 7 _ |a 10.34734/FZJ-2024-04731
|2 datacite_doi
037 _ _ |a FZJ-2024-04731
100 1 _ |a Yu, Jiaao
|0 P:(DE-HGF)0
|b 0
111 2 _ |a 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
|c Milano
|d 2023-10-25 - 2023-10-27
|w Italy
245 _ _ |a Analog Feedback-Controlled Memristor Programming Circuit for Analog Content Addressable Memory
260 _ _ |c 2023
|b IEEE
300 _ _ |a 983-988
336 7 _ |a CONFERENCE_PAPER
|2 ORCID
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a Output Types/Conference Paper
|2 DataCite
336 7 _ |a Contribution to a conference proceedings
|b contrib
|m contrib
|0 PUB:(DE-HGF)8
|s 1738852219_24194
|2 PUB:(DE-HGF)
520 _ _ |a Recent breakthroughs in associative memories suggest that silicon memories are coming closer to human memories, especially for memristive Content Addressable Memories (CAMs) which are capable to read and write in analog values. However, the Program-Verify algorithm, the state-of-the-art memristor programming algorithm, requires frequent switching between verifying and programming memristor conductance, which brings many defects such as high dynamic power and long programming time. Here, we propose an analog feedback-controlled memristor programming circuit that makes use of a novel look-up table-based (LUT-based) programming algorithm. With the proposed algorithm, the programming and the verification of a memristor can be performed in a single-direction sequential process. Besides, we also integrated a single proposed programming circuit with eight analog CAM (aCAM) cells to build an aCAM array. We present SPICE simulations on TSMC 28nm process. The theoretical analysis shows that 1. A memristor conductance within an aCAM cell can be converted to an output boundary voltage in aCAM searching operations and 2. An output boundary voltage in aCAM searching operations can be converted to a programming data line voltage in aCAM programming operations. The simulation results of the proposed programming circuit prove the theoretical analysis and thus verify the feasibility to program memristors without frequently switching between verifying and programming the conductance. Besides, the simulation results of the proposed aCAM array show that the proposed programming circuit can be integrated into a large array architecture.
536 _ _ |a 5234 - Emerging NC Architectures (POF4-523)
|0 G:(DE-HGF)POF4-5234
|c POF4-523
|f POF IV
|x 0
536 _ _ |a 5233 - Memristive Materials and Devices (POF4-523)
|0 G:(DE-HGF)POF4-5233
|c POF4-523
|f POF IV
|x 1
588 _ _ |a Dataset connected to CrossRef Conference
700 1 _ |a Manea, Paul
|0 P:(DE-Juel1)192242
|b 1
|u fzj
700 1 _ |a Ameli Kalkhouran, Sara
|0 P:(DE-Juel1)191419
|b 2
|u fzj
700 1 _ |a Hizzani, Mohammad
|0 P:(DE-Juel1)190961
|b 3
|u fzj
700 1 _ |a Eldebiky, Amro
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Strachan, John Paul
|0 P:(DE-Juel1)188145
|b 5
|u fzj
773 _ _ |a 10.1109/MetroXRAINE58569.2023.10405732
|v 6
856 4 _ |u https://arxiv.org/abs/2304.11030
856 4 _ |u https://juser.fz-juelich.de/record/1028664/files/2304.11030v1.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1028664
|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 0
|6 P:(DE-HGF)0
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)192242
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)191419
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)190961
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)188145
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
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 1
914 1 _ |y 2024
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
920 1 _ |0 I:(DE-Juel1)PGI-14-20210412
|k PGI-14
|l Neuromorphic Compute Nodes
|x 0
980 _ _ |a contrib
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)PGI-14-20210412
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21