Abstract: During the design of microprocessors,it is a great challenge to efficiently determine an optimal design configuration to meet design specifications,and predictive modeling is a promising technique for efficient design space exploration.In order to improve the practicability of existing predictive modeling techniques,we propose to employ model tree based predictive modeling for multi-core design space exploration.First,a small fraction of design configurations are sampled and simulated.Then,such data are utilized to construct a model characterizing the relationship between design parameters and processor responses by model tree algorithm.Finally,such model could be used to predict the responses of other design configurations,and the optimal design configurations can be found.Experimental results show that,compared with SVM-based and ANN-based predictive models,our approach can improve the prediction accuracy by 74.87% and 38.87%,respectively,with respect to performance prediction,and the prediction accuracy by 2.66% and 16.82%,respectively,with respect to energy prediction.