We propose a designing of multi-layer neural networks using 2D NAND flash memory cell as a high-density and reliable synaptic device. Our operation scheme eliminates the waste of NAND flash cells and allows analogue input values. A 3-layer perceptron network with 40,545 synapses is trained on a MNIST database set using an adaptive weight update method for hardware-based multi-layer neural networks. The conductance response of NAND flash cells is measured and it is shown that the unidirectional conductance response is suitable for implementing multi-layer neural networks using NAND flash memory cells as synaptic devices. Using an online-learning, we obtained higher learning accuracy with NAND synaptic devices compared to that with a memristor-based synapse regardless of weight update methods. Using an adaptive weight update method based on a unidirectional conductance response, we obtained a 94.19% learning accuracy with NAND synaptic devices. This accuracy is comparable to 94.69% obtained by synapses based on the ideal perfect linear device. Therefore, NAND flash memory which is mature technology and has great advantage in cell density can be a promising synaptic device for implementing high-density multi-layer neural networks. INDEX TERMS Neuromorphic, NAND flash memory, deep neural networks (DNNs), synaptic device, deep learning, multi-layer neural networks, hardware-based neural network.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.