@inproceedings{353d05d3f6d04fd186b8c852d834afaa,
title = "A Comparative Study of Classification Methods for Flash Memory Error Rate Prediction",
abstract = "NAND Flash memory has been the fastest growing technology in the history of semiconductors and is now almost ubiquitous in the world of data storage. However, NAND devices are not error-free and the raw bit error rate (RBER) increases as devices are programmed and erase (P-E cycled). This requires the use of error correction codes (ECCs), which operate on chunks of data called codewords. NAND manufacturers specify the number of P-E cycles a device can tolerate (known as endurance) 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. Real data is gathered on millions of codewords and eight machine learning classification methods are compared. A new subsampling method based on the error probability density function is also proposed.",
keywords = "Classification, Error rate prediction, Flash memory, Machine learning, Subsampling",
author = "Barry Fitzgerald and Jeannie Fitzgerald and Conor Ryan and Joe Sullivan",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG.; 3rd International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2018 ; Conference date: 22-02-2018 Through 24-02-2018",
year = "2018",
doi = "10.1007/978-3-319-74690-6_38",
language = "English",
isbn = "9783319746890",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer-Verlag GmbH and Co. KG",
pages = "385--394",
editor = "Mohamed Mostafa and Hassanien, {Aboul Ella} and Mohamed Elhoseny and Tolba, {Mohamed F.}",
booktitle = "The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018)",
address = "Germany",
}