Signature-based calibration of analytical system-level performance models

The Sesame system-level simulation framework targets efficient design space exploration of embedded multimedia systems. Even despite Sesame's efficiency, it would fail to explore large parts of the design space simply because system-level simulation is too slow for this. Therefore, Sesame uses analytical performance models to provide steering to the system-level simulation, guiding it toward promising system architectures and thus pruning the design space. In this paper, we present a mechanism to calibrate these analytical models with the aim to deliver trustworthy estimates. Moreover, we also present some initial evaluation results with respect to the accuracy of our calibration mechanism using a case study with a Motion-JPEG encoder.