Cross-Wired Memristive Crossbar Array for Effective Graph Data Analysis.

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 was fabricated using Pt/Ta 2 O 5 /HfO 2 /TiN (PTHT) memristor with high on/off and self-rectifying characteristics. The structural and device benefits of PTHT cwCBA for enhanced PGR precision were highlighted, and the practical efficacy was demonstrated for two applications. First, it executed a dynamic path-finding algorithm, identifying the shortest paths in a dynamic graph. PTHT cwCBA showed a more accurate inferred distance and ∼1/3800 lower processing complexity than the conventional method. Second, it analyzed the protein-protein interaction (PPI) networks containing self-interacting proteins, which possessed intricate characteristics compared to typical graphs. The PPI prediction results exhibited an average of 30.5 and 21.3% improvement in area under the curve and F1-score, respectively, compared to existing algorithms. This article is protected by copyright. All rights reserved.