- author = "Damien Hogan",
- title = "Genetic programming based predictions and estimations for the endurance and retention of {NAND} flash memory devices",
- school = "University of Limerick",
- year = "2013",
- address = "Ireland",
- month = nov,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://hdl.handle.net/10344/4875",
-
URL = "
https://ulir.ul.ie/bitstream/handle/10344/4875/Hogan_2013_genetics.pdf",
- size = "246 pages",
-
abstract = "The central hypothesis of this thesis is that it is
possible to use a supervised machine learning
technique, Genetic Programming (GP), to make accurate
predictions and estimations regarding the endurance and
retention of multi-level cell NAND Flash Memory
devices. The retention of storage locations, or blocks,
within these devices is the length of time for which
they successfully retain their data, while their
endurance is the number of times they can be programmed
and erased prior to failure. Manufacturers currently
place conservative specifications on their devices
since there is no technique available to quickly
determine the actual endurance and retention
capabilities of blocks within them.
An extensive empirical evaluation of a number of MLC NAND Flash devices is completed, identifying features for use with GP, before expressions are evolved to make predictions and estimations regarding the retention and endurance of blocks. The empirical evaluation highlights the large variations in performance between blocks in different devices of the same specification, and even between blocks within the same device. As well as building a data set for later use with GP, the durations of program and erase operations are identified as features with which to make endurance predictions and estimations, while a relationship between block location and endurance is also established.
GP is employed to evolve binary classification expressions, referred to as retention period classifiers, to predict whether blocks will correctly retain their data for a specified length of time. Following this, endurance classifiers are evolved to predict whether blocks will successfully complete a predefined number of cycles. Finally, symbolic regression expressions are evolved, building on the earlier experiments, to estimate the actual number of cycles each block will complete prior to failure and are referred to as endurance estimators.",
- notes = "Supervisor: Conor Ryan",
