How early and with how little data? using genetic programming to evolve endurance classifiers for MLC NAND flash memory

Authors:
Damien Hogan;Tom Arbuckle;Conor Ryan
Affiliations:
Computer Science and Information Systems, University of Limerick, Ireland;Computer Science and Information Systems, University of Limerick, Ireland;Computer Science and Information Systems, University of Limerick, Ireland
Venue:
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Year:
2013

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Abstract

Despite having a multi-billion dollar market and many operational advantages, Flash memory suffers from a serious drawback, that is, the gradual degradation of its storage locations through use. Manufacturers currently have no method to predict how long they will function correctly, resulting in extremely conservative longevity specifications being placed on Flash devices. We leverage the fact that the durations of two crucial Flash operations, program and erase, change as the chips age. Their timings, recorded at intervals early in chips' working lifetimes, are used to predict whether storage locations will function correctly after given numbers of operations. We examine how early and with how little data such predictions can be made. Genetic Programming, employing the timings as inputs, is used to evolve binary classifiers that achieve up to a mean of 97.88% correct classification. This technique displays huge potential for real-world application, with resulting savings for manufacturers.