Memristive Crossbar Array-Based Probabilistic Graph Modeling

Memristive Crossbar Array-Based Probabilistic Graph Modeling

© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 36(2024), 36 vom: 20. Sept., Seite e2403904
1. Verfasser: Jang, Yoon Ho (VerfasserIn)
Weitere Verfasser: Lee, Soo Hyung, Han, Janguk, Cheong, Sunwoo, Shim, Sung Keun, Han, Joon-Kyu, Ryoo, Seung Kyu, Hwang, Cheol Seong
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article crossbar array (CBA) eigenvector decomposition probabilistic graph modeling self‐rectifying memristor steady‐state estimation
Beschreibung
Zusammenfassung:© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.
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
Beschreibung:Date Revised 18.09.2024
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1521-4095
DOI:10.1002/adma.202403904