An approach to automated hardware/software partitioning using a flexible granularity that is driven by high-level estimation techniques

Hardware/software partitioning is a key issue in the design of embedded sys tems when performance constraints have to be met and chip area and/or power dissipation are critical. For that reason, diverse approaches to automatic hardware/software partitioning have been proposed since the early 1990s, I n all approaches so far, the granularity during partitioning is fixed, i.e. , either small system parts (e.g., base blocks) or large system parts (e.g. , whole functions/processes) can be swapped at once during partitioning in order to find the best hardware/software tradeoff. Since the deployment of a fixed granularity is likely to result in suboptimum solutions, we present the first approach that features a flexible granularity during hardware/so ftware partitioning. Our approach is comprehensive in so far that the estim ation techniques, our multigranularity performance estimation technique des cribed here in detail, that control partitioning, are adapted to the flexib le partitioning granularity. In addition, our multilevel objective function is described. It allows us to tradeoff various design constraints/goals (p erformance/hardware area) against each other. As a result, our approach is applicable to a wider range of applications than approaches with a fixed gr anularity. We also show that our approach is fast and that the obtained har dware/software partitions are much more efficient tin terms of hardware eff ort, for example) than in cases where a fixed granularity is deployed.