Grammar-based optimization of synthesis scenarios

Systems for multi-level logic optimization are usually based on a set of specialized, loosely-related transformations which work on a network representation. The sequence of transformations in a synthesis scenario (script) is crucial for the performance of the whole system. This paper presents the application of a genetic algorithm for automatic tuning of scenarios, and therefore an approach for optimizing the synthesis process itself. We introduce a general context-free grammar to describe the set of relevant scenarios. For such grammars, we develop meaningful genetic operators for the implementation of an evolutionary search. Experiments with an industrial logic synthesis system show that our method can improve the efficiency of manually designed standard scenarios by an average of 8%. We also show that this approach can effectively tune scenarios to particular design styles and/or specific synthesis goals