Stochastic Optimization with Learning for Complex Problems
Continue the development of the learning framework and evaluate incremental learning approaches (Bayesian, etc.)
Framework development is ongoing and a variety of incremental learning approaches are under evaluation, including BayesNets, SVMs, and classical adaptive techniques
Apply the techniques to one or two new problems: standard-cell library generation, optimal clustering for placement
Applied to partitioning (ICCAD97) and BDD sifting (ongoing)
Evaluate the effectiveness of adaptive optimization on other problems: BDD variable sifting
Validated the new algorithm by testing the robustness/generality of the model and doing performance analysis
Determination of the learning sample size and discovery of the window effect of the model
Invented novel approach to tradeoff analysis