Estimating MLC NAND flash endurance: A genetic programming based symbolic regression application

Damien Hogan, Tom Arbuckle, Conor Ryan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationGECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference
Pages1285-1292
Number of pages8
DOIs
Publication statusPublished - 2013
Event2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013 - Amsterdam, Netherlands
Duration: 6 Jul 201310 Jul 2013

Publication series

NameGECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference

Conference

Conference2013 15th Genetic and Evolutionary Computation Conference, GECCO 2013
Country/TerritoryNetherlands
CityAmsterdam
Period6/07/1310/07/13

Keywords

  • Endurance
  • Flash memory
  • Genetic programming
  • NAND
  • Symbolic regression

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