Efficient design space exploration via statistical sampling and AdaBoost learning

Abstract

Design space exploration (DSE) has become a notoriously difficult problem due to the exponentially increasing size of design space of microprocessors and time-consuming simulations. To address this issue, machine learning techniques have been widely employed to build predictive models. However, most previous approaches randomly sample the training set leading to considerable simulation cost and low prediction accuracy. In this paper, we propose an efficient and precise DSE methodology by combining statistical sampling and Adaboost learning technique. The proposed method includes three phases. (1) Firstly, orthogonal design based feature selection is employed to prune design space. (2) Sencondly, an orthogonal array based training data sampling method is introduced to select the representative configurations for simulation. (3) Finally, a new active learning approach ActBoost is proposed to build predictive model. Evaluations demonstrate that the proposed framework is more efficient and precise than state-of-art DSE techniques.