Abstract
NAND flash memory is now almost ubiquitous in the world of data storage. However, NAND wears out as it is used, and manufacturers specify the number of times a device can be rewritten (known as program-erase cycles) very conservatively to account for quality variations within and across devices. This research uses machine learning to predict the true cycling level each part of a NAND device can tolerate, based on measurements taken from the device as it is used. Custom-designed hardware is used to gather millions of data samples and eight machine learning classification methods are compared. The classifier is then optimised using ensemble and knowledge-based techniques. Two new subsampling methods based on the error probability density function are also proposed.
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Fitzgerald, B., Ryan, C., Sullivan, J. (2019). An Optimal Machine Learning Classification Model for Flash Memory Bit Error Prediction. In: Hassanien, A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_5
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DOI: https://doi.org/10.1007/978-3-030-02357-7_5
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