基于在线域自适应的Flash寿命智能预测

Ruixiang Ma, Fei Wu, Changsheng Xie
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引用次数: 1

摘要

针对flash寿命模型由于训练样本较少而泛化能力较差的问题,提出了一种多源集成在线域自适应方案(MSE)。MSE使用多个离线源块来帮助建立在线目标块的生命周期预测模型。MSE将这些块中的信息迁移到目标块中,有效地解决了目标块样本不足的痛点。我们在基于fpga的测试平台上模拟了NAND闪存的实际使用场景。实验结果表明,仅使用少量目标块样本,MSE的预测精度就超过0.91。因此,MSE可以在低开销的情况下提高闪存的空间利用率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Prediction of Flash Lifetime via Online Domain Adaptation
To resolve the low generalization ability of the flash lifetime model caused by a small training sample, we propose a multiple source ensemble online domain adaptation scheme, called MSE. MSE uses multiple offline source blocks to assist in establishing a lifetime prediction model for the online target block. MSE migrates information from these blocks to the target block, effectively solving the pain point of insufficient samples for the target block. We simulate the actual use scenarios of NAND flash on the FPGA-based test platform. Experimental results show that prediction accuracy of MSE exceeds 0.91 using only a small number of samples of the target block. Therefore, MSE can be used to improve the space utilization of the flash with low overhead.
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