An Efficient Wear-level Architecture using Self-adaptive Wear Leveling.

ICPP '20: Proceedings of the 49th International Conference on Parallel Processing(2020)

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摘要
The non-volatile memory (NVM) is becoming the main device of next-generation memory, due to the high density, near-zero standby power, non-volatile and byte-addressable features. The multi-level cell (MLC) technique has been used in non-volatile memory to significantly increase device density and capacity, which however leads to much weaker endurance than the single-level cell (SLC) counterpart. Although wear-leveling techniques can mitigate this weakness in MLC, the improvements upon MLC-based NVM become very limited due to not achieving uniform write distribution before some cells are really worn out. To address this problem, our paper proposes a self-adaptive wear-leveling (SAWL) scheme for MLC-based NVM. The idea behind SAWL is to dynamically tune the wear-leveling granularities and balance the writes across the cells of entire memory, thus achieving suitable tradeoff between the lifetime and cache hit rate. Moreover, to reduce the size of the address-mapping table, SAWL maintains a few recently-accessed mappings in a small on-chip cache. Experimental results demonstrate that SAWL significantly improves the NVM lifetime and the performance, compared with state-of-the-art schemes.
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关键词
architecture,wear-level,self-adaptive
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