Cazorla, Francisco J., Abella, Jaume, Andersson, Jan et al. (24 more authors) (2016) Improving Measurement-Based Timing Analysis through Randomisation and Probabilistic Analysis. In: Digital System Design (DSD), 2016 Euromicro Conference on. Digital System Design (DSD), 2016 Euromicro Conference on, 31 Aug - 02 Sep 2016 IEEE , CYP , pp. 276-285.
Abstract
The use of increasingly complex hardware and software platforms in response to the ever rising performance demands of modern real-time systems complicates the verification and validation of their timing behaviour, which form a time-and-effort-intensive step of system qualification or certification. In this paper we relate the current state of practice in measurement-based timing analysis, the predominant choice for industrial developers, to the proceedings of the PROXIMA project in that very field. We recall the difficulties that the shift towards more complex computing platforms causes in that regard. Then we discuss the probabilistic approach proposed by PROXIMA to overcome some of those limitations. We present the main principles behind the PROXIMA approach as well as the changes it requires at hardware or software level underneath the application. We also present the current status of the project against its overall goals, and highlight some of the principal confidence-building results achieved so far.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © IEEE, 2016. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION UNSPECIFIED |
Depositing User: | Pure (York) |
Date Deposited: | 24 Jan 2017 10:28 |
Last Modified: | 09 Dec 2024 00:21 |
Published Version: | https://doi.org/10.1109/DSD.2016.22 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/DSD.2016.22 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:111141 |