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RBER-Aware Lifetime Prediction Scheme for 3D-TLC NAND Flash Memory
Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Hubei, Peoples R China..
Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Hubei, Peoples R China.;Huazhong Univ Sci & Technol, Shenzhen Res Inst, Shenzhen 518000, Peoples R China..
Huazhong Univ Sci & Technol, Wuhan Natl Lab Optoelect, Wuhan 430074, Hubei, Peoples R China..
KTH, School of Electrical Engineering and Computer Science (EECS), Electronics, Electronic and embedded systems.ORCID iD: 0000-0003-0061-3475
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2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 44696-44708Article in journal (Refereed) Published
Abstract [en]

NAND flash memory is widely used in various computing systems. However, flash blocks can sustain only a limited number of program/erase (P/E) cycles, which are referred to as the endurance. On one hand, in order to ensure data integrity, flash manufacturers often define the maximum P/E cycles of the worst block as the endurance of flash blocks. On the other hand, blocks exhibit large endurance variations, which introduce two serious problems. The first problem is that the error correcting code (ECC) is often over-provisioned, as it has to be designed to tolerate the worst case to ensure data integrity, which causes longer decoding latency. The second problem is the underutilized block's lifespan due to conservatively defined block endurance. Raw bit error rate (RBER) of most blocks have not arrived the allowable RBER based on the nominal endurance point, which implies that the conventional P/E cycle-based block retirement policies may waste large flash storage space. In this paper, to exploit the storage capacity of each flash block, we propose an RBER-aware lifetime prediction scheme based on machine learning technologies. We consider the problem that the model can lose prediction effectiveness over time and use incremental learning to update the model for adapting the changes at different lifetime stages. At run time, trained data will be gradually discarded, which can reduce memory overhead. For evaluating our purpose, four well-known machine learning techniques have been compared in terms of predictive accuracy and time overhead under our proposed lifetime prediction scheme. We also compared the predicted values with the tested values obtained in the real NAND flash-based test platform, and the experimental results show that the support vector machine (SVM) models based on our proposed lifetime prediction scheme can achieve as high as 95% accuracy for flash blocks. We also apply our proposed lifetime prediction scheme to predict the actual endurance of flash blocks at four different retention times, and the experimental results show that it can significantly improve the maximum P/E cycle of flash blocks from 37.5% to 86.3% on average. Therefore, the proposed lifetime prediction scheme can provide a guide for block endurance prediction.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 7, p. 44696-44708
Keywords [en]
NAND flash, P/E cycle, retention time, RBER, machine learning
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-251293DOI: 10.1109/ACCESS.2019.2909567ISI: 000465384700001Scopus ID: 2-s2.0-85064569653OAI: oai:DiVA.org:kth-251293DiVA, id: diva2:1314875
Note

QC 20190510

Available from: 2019-05-10 Created: 2019-05-10 Last updated: 2019-06-11Bibliographically approved

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Lu, Zhonghai

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