Estimating MLC NAND flash endurance: a genetic programming based symbolic regression application
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Hogan:2013:GECCO,
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author = "Damien Hogan and Tom Arbuckle and Conor Ryan",
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title = "Estimating MLC NAND flash endurance: a genetic
programming based symbolic regression application",
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booktitle = "GECCO '13: Proceeding of the fifteenth annual
conference on Genetic and evolutionary computation
conference",
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year = "2013",
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editor = "Christian Blum and Enrique Alba and Anne Auger and
Jaume Bacardit and Josh Bongard and Juergen Branke and
Nicolas Bredeche and Dimo Brockhoff and
Francisco Chicano and Alan Dorin and Rene Doursat and
Aniko Ekart and Tobias Friedrich and Mario Giacobini and
Mark Harman and Hitoshi Iba and Christian Igel and
Thomas Jansen and Tim Kovacs and Taras Kowaliw and
Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and
John McCall and Alberto Moraglio and
Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and
Gustavo Olague and Yew-Soon Ong and
Michael E. Palmer and Gisele Lobo Pappa and
Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and
Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and
Daniel Tauritz and Leonardo Vanneschi",
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isbn13 = "978-1-4503-1963-8",
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pages = "1285--1292",
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keywords = "genetic algorithms, genetic programming",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2463372.2463537",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "NAND Flash memory is a multi-billion dollar industry
which is projected to continue to show significant
growth until at least 2017. Devices such as
smart-phones, tablets and Solid State Drives use NAND
Flash since it has numerous advantages over Hard Disk
Drives including better performance, lower power
consumption, and lower weight. However, storage
locations within Flash devices have a limited working
lifetime, as they slowly degrade through use,
eventually becoming unreliable and failing. The number
of times a location can be programmed is termed its
endurance, and can vary significantly, even between
locations within the same device. There is currently no
technique available to predict endurance, resulting in
manufacturers placing extremely conservative
specifications on their Flash devices. We perform
symbolic regression using Genetic Programming to
estimate the endurance of storage locations, based only
on the duration of program and erase operations
recorded from them. We show that the quality of
estimations for a device can be refined and improved as
the device continues to be used, and investigate a
number of different approaches to deal with the
significant variations in the endurance of storage
locations. Results show this technique's huge potential
for real-world application.",
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notes = "Also known as \cite{2463537} GECCO-2013 A joint
meeting of the twenty second international conference
on genetic algorithms (ICGA-2013) and the eighteenth
annual genetic programming conference (GP-2013)",
- }
Genetic Programming entries for
Damien Hogan
Tom Arbuckle
Conor Ryan
Citations