From In-Place Updates to Out-of-Place Selections: Reconsidering Write Disturbance in Non-Volatile Memory

Non-volatile memory (NVM) opens up new opportunities to resolve scaling restrictions of main memory, yet it is still hindered by the write disturbance (WD) problem. The WD problem mistakenly transforms the values of NVM cells, hence seriously deteriorating memory reliability and downgrading access performance. Existing studies mainly mitigate the WD problem via encoding WD-prone data patterns under in-place updates, yet we find that when turning to out-of-place updates, they can gain the potential to reduce more WD errors. We present LearnWD, an approach that mitigates the WD problem in NVM via coupling machine learning with out-of-place updates. LearnWD first employs clustering algorithms to classify the stale data based on the error proneness. To perform a write operation, LearnWD carefully examines the aggressivity of new data and the error proneness of stale data, so as to speculatively minimize the resulting WD errors. We conduct extensive experiments using 15 real-world datasets with different data types, showing that LearnWD can assist a variety of data encoding schemes to further reduce 19.5% of WD errors, shorten 10.1% of write latency, and extend 22.2% of write endurance.