An architectural co-synthesis algorithm for distributed, embedded computing systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Reliable estimation of execution time of embedded software
Proceedings of the conference on Design, automation and test in Europe
Performance analysis with confidence intervals for embedded software processes
Proceedings of the 14th international symposium on Systems synthesis
Process Scheduling for Performance Estimation and Synthesis of Hardware/Software Systems
EUROMICRO '98 Proceedings of the 24th Conference on EUROMICRO - Volume 1
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Actor-oriented models for codesign: balancing re-use and performance
Formal methods and models for system design
High-performance timing simulation of embedded software
Proceedings of the 45th annual Design Automation Conference
A very fast and quasi-accurate power-state-based system-level power modeling methodology
ARCS'12 Proceedings of the 25th international conference on Architecture of Computing Systems
Robust Estimation of Timing Yield With Partial Statistical Information on Process Variations
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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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.