Power exploration for dynamic data types through virtual memory management refinement

In this paper we present our novel power exploration methodology for applications with dynamic data types. Our methodology is crucial to obtain effective solutions in an embedded (HW or SW) processor context. The contributions are twofold. First we define the complete search space for virtual memory management (VMM) mechanisms in a structured way with orthogonal decision trees. Secondly we present our systematic methodology for exploration of the maximal power that takes into account characteristics of the application to heavily prune the search space guiding the choices of a VMM mechanism. Finally we demonstrate for two industrial examples that power can vary considerably depending on the VMM chosen. Moreover these experiments show the effectiveness of our exploration methodology.