An efficient binary programming method for black-box optimization and its application in processor design

Optimizing the parameter settings in a large design space for the processor with limited simulation resources is a challenging task. The current black-box optimization algorithms for processor design space exploration (DSE) problems usually require a large amount of simulation resources for high-dimensional and discrete problems. Besides, the constraints handling techniques in these algorithms need to be improved. To address the issues, we propose an efficient binary integer programming (BIP) approach for the DSE of the processor with strictly guaranteed constraints. Our approach involves adopting the separability assumption to establish a surrogate objective function that is ordinal consistent, thus avoiding the complex non-linearity of the real objective function. Moreover, the design rules can be taken simply as constraints in BIP model to further reduce the design space. Thus, the efforts spent in the infeasible exploration space can be avoided. The experimental results show that the proposed algorithm outperforms the state-of-the-art Bayesian optimization and evolutionary algorithms in terms of exploration efficiency, required simulation points and performance of the recommended points.