On robust task-accurate performance estimation

Beitrag bei einer Tagung

Details zur Publikation

Autorinnen und Autoren: Xu Y, Wang B, Hasholzner R, Rosales R, Teich J
Jahr der Veröffentlichung: 2013
Tagungsband: Proceedings of the 50th Design Automation Conference (DAC 2013)
Seitenbereich: 171-177
ISBN: 9781450320719


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 worst-case 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. Copyright © 2013 ACM.

FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Teich, Jürgen Prof. Dr.-Ing.
Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)

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Xu, Y., Wang, B., Hasholzner, R., Rosales, R., & Teich, J. (2013). On robust task-accurate performance estimation. In Proceedings of the 50th Design Automation Conference (DAC 2013) (pp. 171-177). Austin, TX.

Xu, Yang, et al. "On robust task-accurate performance estimation." Proceedings of the 50th Annual Design Automation Conference, DAC 2013, Austin, TX 2013. 171-177.


Zuletzt aktualisiert 2018-17-10 um 21:50