Abstract

Successive, well organized application of transformations has been widely recognized as an exceptionally effective, but complex and difficult CAD task. We introduce a new potential-driven statistical approach for ordering transformations. Two new synthesis ideas are the backbone of the approach. The first idea is to quantify the characteristics of all transformations and the relationship between them based on their potential to reorganize a computation such that the complexity of the corresponding implementation is reduced. The second one is based on the observation that transformations may disable each other not only because they prevent the application of the other transformation, but also because both transformations target the same potential of the computation. These two observations drastically reduce the search space to find efficient and effective scripts for ordering transformations. A key algorithmic novelty is that both conceptual and optimization insights as well as all opti...

Citations