Cross-Wired Memristive Crossbar Array for Effective Graph Data Analysis

© 2023 Wiley‐VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 36(2024), 13 vom: 27. März, Seite e2311040
1. Verfasser: Jang, Yoon Ho (VerfasserIn)
Weitere Verfasser: Han, Janguk, Shim, Sung Keun, Cheong, Sunwoo, Lee, Soo Hyung, Han, Joon-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 cross‐wiring graph data structure protein–protein interaction self‐rectifying memristor
Beschreibung
Zusammenfassung:© 2023 Wiley‐VCH GmbH.
Graphs adequately represent the enormous interconnections among numerous entities in big data, incurring high computational costs in analyzing them with conventional hardware. Physical graph representation (PGR) is an approach that replicates the graph within a physical system, allowing for efficient analysis. This study introduces a cross-wired crossbar array (cwCBA), uniquely connecting diagonal and non-diagonal components in a CBA by a cross-wiring process. The cross-wired diagonal cells enable cwCBA to achieve precise PGR and dynamic node state control. For this purpose, a cwCBA is fabricated using Pt/Ta2O5/HfO2/TiN (PTHT) memristor with high on/off and self-rectifying characteristics. The structural and device benefits of PTHT cwCBA for enhanced PGR precision are highlighted, and the practical efficacy is demonstrated for two applications. First, it executes a dynamic path-finding algorithm, identifying the shortest paths in a dynamic graph. PTHT cwCBA shows a more accurate inferred distance and ≈1/3800 lower processing complexity than the conventional method. Second, it analyzes the protein-protein interaction (PPI) networks containing self-interacting proteins, which possess intricate characteristics compared to typical graphs. The PPI prediction results exhibit an average of 30.5% and 21.3% improvement in area under the curve and F1-score, respectively, compared to existing algorithms
Beschreibung:Date Revised 28.03.2024
published: Print-Electronic
Citation Status PubMed-not-MEDLINE
ISSN:1521-4095
DOI:10.1002/adma.202311040