ADLPT: Improving 3D NAND Flash Memory Reliability by Adaptive Lifetime Prediction Techniques

NAND flash memory has become increasingly popular in various computing systems. Although NAND flash memory offers attractive performance, it suffers limited operable programming and erasing cycles. To improve the reliability of flash-based systems, previous works introduce machine learning models to predict flash lifetime. These works generally focus on improving prediction accuracy but present little research about the resources required for flash lifetime prediction. In application scenarios, the overheads and the frequency of lifetime predictions are important for storage systems. Excessive prediction actions would lead to unnecessary resource consumption. For building an efficient storage system, resource requirements need to be taken into consideration when designing flash lifetime prediction schemes. In this paper, we propose adaptive lifetime prediction techniques (ADLPT) that minimize redundant prediction operations by exploiting reliability variation. To explore reliability variation, we investigate the error distribution of different 3D flash chips. Based on the investigation, a prediction judgment method is presented. The method identifies the necessary prediction by detecting the variation of erase duration and raw bit errors. Furthermore, we provide a method to improve the performance of the static model. The experimental result shows that our approach can reduce about 90% of redundant predictions with over 0.8 F1-Score.