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Reseach Article

Machine Learning Adaptation in Post Silicon Server Validation

by Pridhiviraj Paidipeddi, Dheerendra Singh Tomar
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 11
Year of Publication: 2014
Authors: Pridhiviraj Paidipeddi, Dheerendra Singh Tomar
10.5120/ijais14-451252

Pridhiviraj Paidipeddi, Dheerendra Singh Tomar . Machine Learning Adaptation in Post Silicon Server Validation. International Journal of Applied Information Systems. 7, 11 ( November 2014), 11-14. DOI=10.5120/ijais14-451252

@article{ 10.5120/ijais14-451252,
author = { Pridhiviraj Paidipeddi, 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 = { 11 },
month = { November },
year = { 2014 },
issn = { 2249-0868 },
pages = { 11-14 },
numpages = {9},
url = { https://www.ijais.org/archives/volume7/number11/693-1252/ },
doi = { 10.5120/ijais14-451252 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T18:55:48.907070+05:30
%A Pridhiviraj Paidipeddi
%A Dheerendra Singh Tomar
%T Machine Learning Adaptation in Post Silicon Server Validation
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 7
%N 11
%P 11-14
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
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
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  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
Index Terms

Computer Science
Information Sciences

Keywords

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