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240721s2024 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202403904
|2 doi
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|a pubmed24n1538.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Jang, Yoon Ho
|e verfasserin
|4 aut
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| 245 |
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|a Memristive Crossbar Array-Based Probabilistic Graph Modeling
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|c 2024
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Revised 18.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.
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|a Modern graph datasets with structural complexity and uncertainties due to incomplete information or data variability require advanced modeling techniques beyond conventional graph models. This study introduces a memristive crossbar array (CBA)-based probabilistic graph model (C-PGM) utilizing Cu0.3Te0.7/HfO2/Pt memristors, which exhibit probabilistic switching, self-rectifying, and memory characteristics. C-PGM addresses the complexities and uncertainties inherent in structural graph data across various domains, leveraging the probabilistic nature of memristors. C-PGM relies on the device-to-device variation across multiple memristive CBAs, overcoming the limitations of previous approaches that rely on sequential operations, which are slower and have a reliability concern due to repeated switching. This new approach enables the fast processing and massive implementation of probabilistic units at the expense of chip area. In this study, the hardware-based C-PGM feasibly expresses small-scale probabilistic graphs and shows minimal error in aggregate probability calculations. The probability calculation capabilities of C-PGM are applied to steady-state estimation and the PageRank algorithm, which is implemented on a simulated large-scale C-PGM. The C-PGM-based steady-state estimation and PageRank algorithm demonstrate comparable accuracy to conventional methods while significantly reducing computational costs
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|a Journal Article
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|a crossbar array (CBA)
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4 |
|a eigenvector decomposition
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| 650 |
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4 |
|a probabilistic graph modeling
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| 650 |
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4 |
|a self‐rectifying memristor
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| 650 |
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4 |
|a steady‐state estimation
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| 700 |
1 |
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|a Lee, Soo Hyung
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Han, Janguk
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cheong, Sunwoo
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shim, Sung Keun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Han, Joon-Kyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ryoo, Seung Kyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hwang, Cheol Seong
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 36(2024), 36 vom: 20. Sept., Seite e2403904
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
1 |
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|g volume:36
|g year:2024
|g number:36
|g day:20
|g month:09
|g pages:e2403904
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|u http://dx.doi.org/10.1002/adma.202403904
|3 Volltext
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