TY - GEN
T1 - How early and with how little data? Using genetic programming to evolve endurance classifiers for MLC NAND flash memory
AU - Hogan, Damien
AU - Arbuckle, Tom
AU - Ryan, Conor
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Binary Classifier
KW - Flash Memory
KW - Genetic Programming
UR - http://www.scopus.com/inward/record.url?scp=84875104863&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37207-0_22
DO - 10.1007/978-3-642-37207-0_22
M3 - Conference contribution
AN - SCOPUS:84875104863
SN - 9783642372063
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 253
EP - 264
BT - Genetic Programming - 16th European Conference, EuroGP 2013, Proceedings
T2 - 16th European Conference on Genetic Programming, EuroGP 2013
Y2 - 3 April 2013 through 5 April 2013
ER -