Optimising Flash Memory for Differing Usage Scenarios: Goals and Approach

Arbuckle, Tom; Hogan, Damien; Ryan, Conor

doi:10.1007/978-3-642-32692-9_18

Cited by 3 publications

(1 citation statement)

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“…This group also examined the potential use of retention classifiers [14]. Arbuckle et al provide overviews of this and related work [2,1] and also compare a number of statistical and machine learning techniques for the purpose of endurance classification [3].…”

Section: Related Workmentioning

confidence: 99%

Estimating MLC NAND flash endurance

Hogan

Arbuckle

Ryan

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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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.

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Section: Related Workmentioning

confidence: 99%