Probabilistic application modeling for system-level performance analysis

The objective of this paper is to introduce the Stochastic Automata Networks (SANs) as an effective formalism for application modeling in system-level analysis. More precisely we present a methodology for application modeling for system-level power/performance analysis that can help the designer to select the right platform and implement a set of target multimedia applications. We also show that, under various input traces, the steady-state behavior of the application itself is characterized by very different `clusterings´ of the probability distributions. Having this information available, not only helps to avoid lengthy profiling simulations for predicting power and performance figures, but also enables efficient mappings of the applications onto a chosen platform. We illustrate the benefits of our methodology using the MPEG-2 video decoder as the driver application