Efficient design space exploration by knowledge transfer

Due to the exponentially increasing size of design space of microprocessors and time-consuming simulations, predictive models have been widely employed in design space exploration (DSE). Traditional approaches mostly build a program-specific predictor that needs a large number of program-specific samples. Thus considerable simulation cost is required for each program. In this paper, we study the novel problem of transferring knowledge from the labeled samples of previous programs to help predict the responses of the new target program whose labeled samples are very sparse. Inspired by the recent advances of transfer learning, we propose a transfer learning based DSE framework TrDSE to build a more efficient and effective predictive model for the target program with only a few simulations by borrowing knowledge from previous programs. Specifically, TrDSE includes two phases: 1) clustering the programs based on the proposed orthogonal array sampling and the distribution related features, and 2) with the guidance of clustering results, predicting the responses of configurations in design space of the target program by a transfer learning based regression algorithm. We evaluate the proposed TrDSE on the benchmarks of SPEC CPU 2006 suite. The results demonstrate that the proposed framework is more efficient and effective than state-of-art DSE techniques.