Advertisement

An Optimal Machine Learning Classification Model for Flash Memory Bit Error Prediction

  • Barry Fitzgerald
  • Conor Ryan
  • Joe Sullivan
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 801)

Abstract

NAND flash memory is now almost ubiquitous in the world of data storage. However, NAND wears out as it is used, and manufacturers specify the number of times a device can be rewritten (known as program-erase cycles) very conservatively to account for quality variations within and across devices. This research uses machine learning to predict the true cycling level each part of a NAND device can tolerate, based on measurements taken from the device as it is used. Custom-designed hardware is used to gather millions of data samples and eight machine learning classification methods are compared. The classifier is then optimised using ensemble and knowledge-based techniques. Two new subsampling methods based on the error probability density function are also proposed.

Keywords

Flash memory Machine learning Error rate prediction Classification Subsampling Classifier ensemble 

References

  1. 1.
    Arbuckle, T., Hogan, D., Ryan, C.: Learning predictors for flash memory endurance: A comparative study of alternative classification methods. Int. J. Comput. Intell. Stud. 3(1), 18–39 (2014)CrossRefGoogle Scholar
  2. 2.
    Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl. 6(1), 20–29 (2004)Google Scholar
  3. 3.
    Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)zbMATHGoogle Scholar
  4. 4.
    Fitzgerald, B., Fitzgerald, J., Ryan, C., Sullivan, J.: A comparative study of classification methods for flash memory error rate prediction. In: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), pp. 385–394. Springer International Publishing, Cham (2018)Google Scholar
  5. 5.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Friedman, J., Hastie, T., Tibshirani, R.: Special invited paper. Additive logistic regression: a statistical view of boosting: rejoinder. Ann. Stat. 28(2), 400–407 (2000)Google Scholar
  7. 7.
    Hogan, D., Arbuckle, T., Ryan, C.: Evolving a storage block endurance classifier for flash memory: a trial implementation. In: 2012 IEEE 11th International Conference on Cybernetic Intelligent Systems (CIS), pp. 12–17 (2012)Google Scholar
  8. 8.
    Huang, Y.S., Suen, C.Y.: A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans. Pattern Anal. Mach. Intell. 17(1), 90–94 (1995)CrossRefGoogle Scholar
  9. 9.
    Jesd218a—solid state drive (SSD) requirements and endurance test method. Standard, JEDEC (2011)Google Scholar
  10. 10.
    Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 179–186. Morgan Kaufmann (1997)Google Scholar
  11. 11.
    Lee, J.D., Choi, J.H., Park, D., Kim, K.: Degradation of tunnel oxide by fn current stress and its effects on data retention characteristics of 90 nm nand flash memory cells. In: Reliability Physics Symposium Proceedings, 2003. 41st Annual. 2003 IEEE International, pp. 497–501 (2003)Google Scholar
  12. 12.
    Mason, L., Baxter, J., Bartlett, P., Frean, M.: Boosting algorithms as gradient descent. In: Proceedings of the 12th International Conference on Neural Information Processing Systems, NIPS’99, pp. 512–518. MIT Press, Cambridge, MA, USA (1999)Google Scholar
  13. 13.
    McKay, M.D., Beckman, R.J., Conover, W.J.: Comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2), 239–245 (1979)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Mielke, N., Marquart, T., Wu, N., Kessenich, J., Belgal, H., Schares, E., Trivedi, F., Goodness, E., Nevill, L.R.: Bit error rate in NAND flash memories. In: 2008 IEEE International Reliability Physics Symposium, pp. 9–19 (2008)Google Scholar
  15. 15.
    Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990)Google Scholar
  16. 16.
    Yang, X., Song, Q., Cao, A.: Weighted support vector machine for data classification. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005., vol. 2, pp. 859–864 vol. 2 (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Limerick Institute of TechnologyLimerickIreland
  2. 2.University of LimerickLimerickIreland

Personalised recommendations