Prediction and predictability of optimization heuristics are at the foundation of convergent, top-down system synthesis. Not knowing how a heuristic behaves on various types of relevant instances makes accurate modeling, and hence predictability, difficult. This project is exploring basic ideas that can lead to improved understanding of predictability of heuristic algorithm behavior within top-down physical design. The project first develops a new methodology and criteria for characterizing the operation of given (iterative, combinatorial) heuristic functions, and how the application context and use model define requirements for the design, implementation and evaluation of the heuristic. The project also explores principles for developing predictors of a given heuristic's output, based on understanding of the heuristic and its context. Finally, in the context of a multi-stage optimization ("design flow"), the project addresses means of abstracting objectives that can be effectively optimized from downstream parameters of the design state.
