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Self-organizing Map (SOM) neural network is a prominent algorithm in unsupervised machine learning, which is widely used for data clustering, high-dimensional visualization, and feature extraction. However, the hardware implementation of SOM is limited by the von Neumann bottleneck. Herein, a SOM neural network is implemented by the combination of 3D NAND flash memory arrays and in-memory Euclidean distance (ED) calculation. The weights in the SOM network are mapped to the conductance of the 3D NAND differential pair. It is experimentally demonstrated that the differential pair in 3D NAND flash array possesses superior characteristics for neuromorphic computing during increasing and decreasing synaptic weight. Using the 3D NAND-based SOM, a competitive learning neural network is established and used for the unsupervised classification of a set of Gaussian distribution data points. The experimental results illustrate the excellent performance and efficiency of the proposed architecture, highlighting the potential of 3D NAND-based in-memory computing for artificial intelligence applications.

Self-Organizing Mapping Neural Network Implementation Based on 3D NAND Flash for Competitive Learning