Near-Free Lifetime Extension for 3-D nand Flash via Opportunistic Self-Healing

3-D <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nand</small> flash memories are the dominant storage media in modern data centers due to their high performance, large storage capacity, and low-power consumption. However, the lifetime of flash memory has decreased as technology scaling advances. Recent work has revealed that the number of achievable program/erase (P/E) cycles of flash blocks is related to the dwell time (DT) between two adjacent erase operations. A longer DT can lead to higher-achievable P/E cycles and, therefore, a longer lifetime for flash memories. This article found that the achievable P/E cycles would increase when flash blocks endure uneven DT distribution. Based on this observation, this article presents an opportunistic self-healing method to extend the lifetime of flash memory. By maintaining two groups with unequal block counts, namely, Active Group and Healing Group, the proposed method creates an imbalance in erase operation distribution. The Active Group undergoes more frequent erase operations, resulting in shorter DT, while the Healing Group experiences longer DT. Periodically, the roles of the two groups are switched based on the Active Group’s partitioning ratio. This role switching ensures that each block experiences both short and long DT periods, leading to an uneven DT distribution that magnifies the self-healing effect. The evaluation shows that the proposed method can improve the flash lifetime by 19.3% and 13.2% on average with near-free overheads, compared with the baseline and the related work, respectively.

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Publication Details

Journal
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Published
2024-11-01
DOI
https://doi.org/10.1109/tcad.2024.3447225
Citations
1
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.35

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article

Near-Free Lifetime Extension for 3-D nand Flash via Opportunistic Self-Healing

Nan Guan, Yina Lv, Min Ye, Tianyu Ren et al.
1 citations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Neural Networks and Reservoir Computing
0.35
article

Near-Free Lifetime Extension for 3-D nand Flash via Opportunistic Self-Healing

Nan Guan, Yina Lv, Min Ye, Tianyu Ren, Qiao Li, Chun Jason Xue
article en
1 citations

Abstract

3-D <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nand</small> flash memories are the dominant storage media in modern data centers due to their high performance, large storage capacity, and low-power consumption. However, the lifetime of flash memory has decreased as technology scaling advances. Recent work has revealed that the number of achievable program/erase (P/E) cycles of flash blocks is related to the dwell time (DT) between two adjacent erase operations. A longer DT can lead to higher-achievable P/E cycles and, therefore, a longer lifetime for flash memories. This article found that the achievable P/E cycles would increase when flash blocks endure uneven DT distribution. Based on this observation, this article presents an opportunistic self-healing method to extend the lifetime of flash memory. By maintaining two groups with unequal block counts, namely, Active Group and Healing Group, the proposed method creates an imbalance in erase operation distribution. The Active Group undergoes more frequent erase operations, resulting in shorter DT, while the Healing Group experiences longer DT. Periodically, the roles of the two groups are switched based on the Active Group’s partitioning ratio. This role switching ensures that each block experiences both short and long DT periods, leading to an uneven DT distribution that magnifies the self-healing effect. The evaluation shows that the proposed method can improve the flash lifetime by 19.3% and 13.2% on average with near-free overheads, compared with the baseline and the related work, respectively.

IEEE Transactions on Computer-Aided Design of Integrated Circuits and SystemsVol. 43(11)
City University of Hong Kong (HK), Xiamen University (CN), Mohamed bin Zayed University of Artificial Intelligence (AE)
Natural Science Foundation of Xiamen City, National Natural Science Foundation of China
Openalex Percentile: Top 32%
Neural Networks and Reservoir Computing
0.35
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