- author = "Damien Hogan and Tom Arbuckle and Conor Ryan",
- title = "Evolving a storage block endurance classifier for Flash memory: A trial implementation",
- booktitle = "11th IEEE International Conference on Cybernetic Intelligent Systems (CIS 2012)",
- year = "2012",
- month = "22-23 " # aug,
- pages = "12--17",
- address = "Limerick",
- keywords = "genetic algorithms, genetic programming, Testing",
-
DOI = "
doi:10.1109/CIS.2013.6782154",
-
abstract = "Solid State Drives (SSDs) have a number of significant
advantages over traditional Hard Disk Drives (HDDs) but
are currently far more expensive and have smaller
capacities. These drives are based on NAND Flash memory
devices, which have limited working lives. The number
of times locations in such devices can be successfully
programmed before they become unreliable is termed
their endurance.
There is currently no way to estimate accurately when a location within a Flash device will fail, so manufacturers give extremely conservative guarantees about the number of program operations their chips can endure. This paper describes a trial implementation of Genetic Programming (GP) used to evolve a Binary Classifier that predicts whether storage blocks within Flash memory devices will still be functioning correctly beyond some predefined number of cycles. The classifier is supplied with only the measured program and erase times from a relatively early point in the lifetime of a block. Using the relationships between these times, the system can accurately predict whether the block will continue to function satisfactorily up to a required number of cycles. Experiments on test sets comprised of unseen data show that our classifier obtains up to an average of 95percent accuracy across 30 runs.",
- notes = "Also known as \cite{6782154}",
