The proliferation of information has facilitated international accessibility, which calls for escalating quantities of energy for knowledge storage and processing functions. Lately, there was an increase in analysis within the discipline of neuromorphic electronics, which pulls inspiration from organic neurons and synapses. These electronics possess the flexibility to carry out in-memory computing, which helps alleviate the restrictions imposed by the ‘von Neumann bottleneck’ that exists between the reminiscence and processor within the conventional von Neumann structure. By leveraging their multi-bit non-volatility, traits that mimic biology, and Kirchhoff’s legislation, neuromorphic electronics provide a promising resolution to scale back the ability consumption in processing vector–matrix multiplication duties. Amongst all the prevailing nonvolatile reminiscence applied sciences, NAND flash reminiscence is likely one of the best built-in options for the storage of enormous volumes of information. This work supplies a complete overview of the current developments in neuromorphic computing primarily based on NAND flash reminiscence. Neuromorphic architectures utilizing NAND flash reminiscence for off-chip studying are offered with numerous quantization ranges of enter and weight. Subsequent, neuromorphic architectures for on-chip studying are offered utilizing customary backpropagation and suggestions alignment algorithms. The array structure, operation scheme, and electrical traits of NAND flash reminiscence are mentioned with a give attention to the usage of NAND flash reminiscence in numerous neural community buildings. Moreover, the discrepancy of array structure between on-chip studying and off-chip studying is addressed. This evaluation article supplies a basis for understanding the neuromorphic computing primarily based on the NAND flash reminiscence and strategies to put it to use primarily based on software necessities.
