Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Characterizing flash memory: anomalies, observations, and applications
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
Empirical evaluation of NAND flash memory performance
ACM SIGOPS Operating Systems Review
Improving symbolic regression with interval arithmetic and linear scaling
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Write endurance in flash drives: measurements and analysis
FAST'10 Proceedings of the 8th USENIX conference on File and storage technologies
How i learned to stop worrying and love flash endurance
HotStorage'10 Proceedings of the 2nd USENIX conference on Hot topics in storage and file systems
An overview of non-volatile memory technology and the implication for tools and architectures
Proceedings of the Conference on Design, Automation and Test in Europe
A destructive evolutionary algorithm process
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Bio-inspired Learning and Intelligent Systems
Error Correction Codes for Non-Volatile Memories
Error Correction Codes for Non-Volatile Memories
Exploiting memory device wear-out dynamics to improve NAND flash memory system performance
FAST'11 Proceedings of the 9th USENIX conference on File and stroage technologies
The bleak future of NAND flash memory
FAST'12 Proceedings of the 10th USENIX conference on File and Storage Technologies
Optimising Flash non-volatile memory using machine learning: a project overview
Proceedings of the Fifth Balkan Conference in Informatics
Inside Solid State Drives (SSDs)
Inside Solid State Drives (SSDs)
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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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.