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Content Provider | IEEE Xplore Digital Library |
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Author | Yang Xu Bo Wang Hasholzner, R. Rosales, R. Teich, J. |
Copyright Year | 2013 |
Description | Author affiliation: Intel Mobile Commun., Munich, Germany (Yang Xu; Bo Wang; Hasholzner, R.) || Univ. of Erlangen-Nuremberg, Erlangen, Germany (Rosales, R.; Teich, J.) |
Abstract | Task-accurate performance estimation methods are widely applied in early design phases to explore different architecture options. These methods rely on accurate annotations generated by software profiling or real measurements to guarantee accurate results. However, in practice, such accurate annotations are not available in early design phases due to lack of source code and hardware platform. Instead, estimated mean or worstcase annotations are usually used, which makes the final result inaccurate because of the errors induced by the estimations, especially for designs with tight time constraints. In this paper, we propose a novel methodology that combines Distributionally Robust Monte Carlo Simulation with task-accurate performance estimation method to guarantee robust system performance estimation in early design phases, i.e., determining the lower bound of the confidence level of fulfilling a specific time constraint. Instead of using accurate annotations, our method only uses estimated annotations in the form of intervals and it does not make any assumptions of the distribution types of these intervals. |
Starting Page | 1 |
Ending Page | 6 |
File Size | 249087 |
Page Count | 6 |
File Format | |
ISBN | 9781450320719 |
ISSN | 0738100X |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2013-05-29 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Association for Computing Machinery, Inc. (ACM) |
Subject Keyword | Estimation Robustness Time factors Mathematical model Decoding Timing Digital audio players distributionally robust Monte Carlo simulation task-accurate robust performance estimation |
Content Type | Text |
Resource Type | Article |
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