Unsupervised Learning in Winner-Takes-All Neural Network Based on 3D NAND Flash

Abstract

Experimental demonstration of unsupervised learning is realized with charge-trapping 3D NAND flash device array. The charge-trapping characteristic of the memory cell is leveraged so that the devices perform not only as non-volatile memory but also synaptic cells in hardware neural network. Experiments reveal that the 3D NAND flash devices hold good potential for neuromorphic computing because of its delicately modulated synaptic weight. Furthermore, differential pair scheme is employed to enable larger dynamic range and finer weight tunability as compared with previous works. And based on the 3D NAND array, unsupervised learning is implemented by experiments with a winner-takes-all neural network, which can achieve perfect clustering of stylized letters within tens of training epochs. Beyond that, the 3D NAND devices also exhibit highly robust reliability against various disturb during synaptic weight update. The excellent synaptic device performance and reliability altogether make solid foundation for the winner-takes-all update rule in unsupervised learning neural network.