Publication date: 24th July 2026
Graph data provides an essential framework for describing complex systems in which the relationships between data points are as important as the data itself. However, conventional graph analysis often requires intensive computation because graph connectivity must be repeatedly searched and updated in software. This work presents a hardware-based computing methodology for graph data analysis using crossbar array structures and memory devices. In the proposed approach, graph nodes and edges are physically mapped onto a crossbar array by controlling the device characteristics in diagonal and non-diagonal regions. Because electrical current naturally flows through paths with lower resistance, the crossbar array can directly evaluate graph connectivity and identify efficient paths in the form of circuit responses. This physical representation of graph structures enables graph-processing functions to be performed at the hardware level, reducing computational overhead and offering a promising route toward next-generation computing systems for complex graph data analysis.
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