potential-driven statistical ordering of transformations
Successive, well organized application of transformations has beenwidely recognized as an exceptionally effective, but complex anddifficult CAD task. We introduce a new potential-driven statisticalapproach for ordering transformations. Two new synthesis ideasare the backbone of the approach. The first idea is to quantifythe characteristics of all transformations and the relationship betweenthem based on their potential to reorganize a computationsuch that the complexity of the corresponding implementation isreduced. The second one is based on the observation that transformationsmay disable each other not only because they prevent theapplication of the other transformation, but also because both transformationstarget the same potential of the computation. These twoobservations drastically reduce the search space to find efficient andeffective scripts for ordering transformations. A key algorithmicnovelty is that both conceptual and optimization insights as well asall optimization algorithms are automatically derived by organizedexperimentation and statistical methods. On a large set of diversereal-life examples improvements in throughput, area, and power bylarge factors have been obtained. Both qualitative and quantitativestatistical analysis indicate effectiveness, high robustness, and consistencyof the new approach for ordering transformations.
statistics order applications effect complexity task synthesis relationships computation implementation observability search space efficiency script algorithm optimality method examples throughput power factor analysis robust