Affiliation:
1. Department of Materials Science and Engineering and Inter‐university Semiconductor Research Center College of Engineering Seoul National University Seoul 08826 Republic of Korea
Abstract
AbstractGraphs 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.
Funder
National Research Foundation of Korea
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献