Hyperplane tree-based data mining with a multi-functional memristive crossbar array

• Published in: Materials horizons Vol. 11; no. 23; pp. 5946 - 5959
• Main Authors: Cheong, Sunwoo, Shin, Dong Hoon, Lee, Soo Hyung, Jang, Yoon Ho, Han, Janguk, Shim, Sung Keun, Han, Joon-Kyu, Ghenzi, Néstor, Hwang, Cheol Seong
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 25.11.2024
• Subjects: Algorithms; Arrays; Binary codes; Clustering; Data analysis; Data mining; Datasets; Digital computers; Hardware; Hyperplanes; Memristors; Outliers (statistics); Spatial data; Task complexity
• ISSN: 2051-6347; 2051-6355; 2051-6355
• DOI: 10.1039/d4mh00942h

This study explores the stochastic and binary switching behaviors of a Ta/HfO 2 /RuO 2 memristor to implement a combined data mining approach for outlier detection and data clustering algorithms in a multi-functional memristive crossbar array. The memristor switches stochastically with high state dispersion in the stochastic mode and deterministically between two states with low dispersion in the binary mode, while they can be controlled by varying operating voltages. The stochastic mode facilitates the parallel generation of random hyperplanes in a tree structure, used to compress spatial information of the dataset in the Euclidian space into binary format, still retaining sufficient spatial features. The ensemble effect from multiple trees improved the classification performance. The binary mode facilitates parallel Hamming distance calculation of the binary codes containing spatial information, which measures similarity. These two modes enable efficient implementation of the newly proposed minority-based outlier detection method and modified K -means method on the same hardware. Array measurements and hardware simulations investigate various hyperparameters' impact and validate the proposed methods with practical datasets. The proposed methods show linear O( n ) time complexity and high energy efficiency, consuming <1% of the energy compared to digital computing with conventional algorithms while demonstrating software-comparable performance in both tasks. A multi-functional memristive crossbar array is studied to implement a newly proposed hyperplane tree-based data mining. The parallelism of the adopted crossbar decreases the time complexity and energy consumption compared to previous methods.