The Characterisation and Optimisation of TLC NAND Flash Memory using Machine Learning - A Position Paper

Sorcha Bennett, Joe Sullivan

Abstract

Flash memory is non-volatile and, while it is becoming ever more commonplace, it is not yet a complete replacement for hard disk drives. The physical layout of Flash means that it is more susceptible to degradation over time, leading to a limited lifetime of use. This paper will give an introduction to NAND Flash memory, followed by an overview of the relevant research on the reliability of MLC memory, conducted using Machine Learning (ML). The results obtained will then be used to characterise and optimise the reliability of TLC memory.

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Paper Citation


in Harvard Style

Bennett S. and Sullivan J. (2013). The Characterisation and Optimisation of TLC NAND Flash Memory using Machine Learning - A Position Paper . In Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-8565-39-6, pages 559-564. DOI: 10.5220/0004330305590564


in Bibtex Style

@conference{icaart13,
author={Sorcha Bennett and Joe Sullivan},
title={The Characterisation and Optimisation of TLC NAND Flash Memory using Machine Learning - A Position Paper},
booktitle={Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2013},
pages={559-564},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004330305590564},
isbn={978-989-8565-39-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - The Characterisation and Optimisation of TLC NAND Flash Memory using Machine Learning - A Position Paper
SN - 978-989-8565-39-6
AU - Bennett S.
AU - Sullivan J.
PY - 2013
SP - 559
EP - 564
DO - 10.5220/0004330305590564