TY - GEN
T1 - Estimating MLC NAND flash endurance
T2 - 2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013
AU - Hogan, Damien
AU - Arbuckle, Tom
AU - Ryan, Conor
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Endurance
KW - Flash memory
KW - Genetic programming
KW - NAND
KW - Symbolic regression
UR - http://www.scopus.com/inward/record.url?scp=84883101522&partnerID=8YFLogxK
U2 - 10.1145/2463372.2463537
DO - 10.1145/2463372.2463537
M3 - Conference contribution
AN - SCOPUS:84883101522
SN - 9781450319638
T3 - GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference
SP - 1285
EP - 1292
BT - GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference
Y2 - 6 July 2013 through 10 July 2013
ER -