| Summary: | The aim of design space exploration (DSE) is to identify implementations with optimal quality characteristics which simultaneously satisfy all imposed design constraints. Hence, besides searching for new solutions, a quality evaluation has to be performed for each design point. This process is typically very expensive and takes a majority of the exploration time. As nearly all the explored design points are sub-optimal, most of them get discarded after evaluation. However, evaluating a solution takes virtually the same amount of time for both good and bad ones. That way, a huge amount of computing power is literally wasted. In this paper, we propose a solution to the aforementioned problem by integrating efficient approximations in the background of a DSE engine in order to allow an initial evaluation of each solution. Only if the approximated quality indicates a promising candidate, the time-consuming exact evaluation is executed. The novelty of our approach is that (1) although the evaluation process is accelerated by using approximations, we do not forfeit the quality of the acquired solutions and (2) the integration in a background theory allows sophisticated reasoning techniques to prune the search space with the help of the approximation results. We have conducted an experimental evaluation of our approach by investigating the dependency of the accuracy of used approximations on the performance gain. Based on 120 electronic system level problem instances, we show that our approach is able to increase the overall exploration coverage by up to six times compared to a conservative DSE whenever accurate approximation functions are available.
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