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Authors: Sorcha Bennett and Joe Sullivan

Affiliation: Limerick Institute of Technology, Ireland

Keyword(s): Non-volatile Memory, Flash Memory, Reliability, Endurance, Retention, Wearout, NOR, NAND, Multi-Level Cell (MLC), Triple-Level Cell (TLC), Machine Learning (ML).

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Industrial Applications of AI ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 559-564. DOI: 10.5220/0004330305590564

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Bennett, S.
AU - Sullivan, J.
PY - 2013
SP - 559
EP - 564
DO - 10.5220/0004330305590564
PB - SciTePress