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Number 11

Machine Learning Adaptation in Post Silicon Server Validation

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  • Pridhiviraj Paidipeddi and Dheerendra Singh Tomar 2014. Machine Learning Adaptation in Post Silicon Server Validation. International Journal of Applied Information Systems. 7, 11 (November 2014), 11-14. DOI=http://dx.doi.org/10.5120/ijais451252 
  • @article{10.5120/ijais2017451568,
author = {Pridhiviraj Paidipeddi and Dheerendra Singh Tomar},
title = {Machine Learning Adaptation in Post Silicon Server Validation},
journal = {International Journal of Applied Information Systems},
issue_date = {November 2014},
volume = {7},
number = {},
month = {November},
year = {2014},
issn = {},
pages = {11-14},
numpages = {},
url = {/archives/volume7/number11/693-1252},
doi = { 10.5120/ijais14-451252},
publisher = { xA9 2013 by IJAIS Journal},
address = {}
}

  • %1 451252
%A Pridhiviraj Paidipeddi
%A Dheerendra Singh Tomar
%T Machine Learning Adaptation in Post Silicon Server Validation
%J International Journal of Applied Information Systems
%@ 
%V 7
%N 
%P 11-14
%D 2014
%I  xA9 2013 by IJAIS Journal

Abstract

As the complexity of modern server processors increases so the validation challenges. Current design validation methods cover less, resulting in bug escape and more regress post-silicon validation. The biggest problem is manual debugging of several failures by large number of test cases. By using machine learning in server validation, validation efforts and resource requirement will reduce. Validation of future generation server will be done through the learning set generated from the previous generation device, which is a set of test cases being passed.

References

  1. Phil Simon (March 18, 2013). Too Big to Ignore: The Business Case for Big Data. Wiley. p. 89. ISBN 978-1118638170.
  2. Mitchell, T. (1997). Machine Learning, McGraw Hill. ISBN 0-07-042807-7, p. 2.
  3. Andrew DeOrio, Qingkun Li, Matthew Burgess and Valeria Bertacco, Machine Learning-based Anomaly
  4. Detection for Post-silicon Bug Diagnosis, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2013, pp. 491.
  5. Wernick, Yang, Brankov, Yourganov and Strother, Machine Learning in Medical Imaging, IEEE Signal Processing Magazine, vol. 27, no. 4, July 2010, pp. 25-38.
  6. Subhasish Mitra , Sanjit A. Seshia, and Nicola Nicolici, Post-Silicon Validation Opportunities, Challenges and Recent Advances. Design Automation Conference (DAC), 2010 47th ACM/IEE

Keywords

Debugging, Learning set, Machine learning, Post-silicon, Server, System under test (SUT), Validation.

Index Terms

Computer Science
Information Sciences