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Parallel Density-Based Spatial Clustering with Dual-Functional Memristive Crossbar Array
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DOI：10.1002/adfm.202309108.png)
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Abstract
En 中文
Analog and digital switching performances in a Ta/HfO2/RuO2 (THR) memristor are studied to implement a density-based spatial clustering of applications with noise (DBSCAN) algorithm in a low-power, parallel-computing memristor crossbar structure. In the analog mode THR memristor, more than 256 states can be stored through a fine-tuning process with a denoising scheme. The analog mode crossbar array facilitates Euclidean distance calculation between any points in the given graphic dataset. In the digital mode, the on/off ratio of more than three orders of magnitude between the binary states is achieved, providing functionality to cluster the data points with a reduced number of operations. The parallel computing capacity of the adopted crossbar decreases the time complexity of the original DBSCAN from O(n2) to O(n). Through array-level simulations, the effectiveness of hardware functionality is validated using representative synthetic datasets and single-cell RNA sequences datasets. Analog and digital switching performances in Ta/HfO2/RuO2 memristor are studied to implement a density-based spatial clustering of applications with noise algorithm in a low-power, parallel-computing memristor crossbar structure. The parallel computing capacity of the adopted crossbar decreases the time complexity of the original DBSCAN from O(n2) to O(n).image
Keywords:
analog Euclidean distance calculation
DBSCAN
dual-mode memristors
memristive crossbar arrays
processing-in-memory
propagation clustering
Journal
IF:
19
Papers:
3.4W
Citations:
32.1W