Cross-program Design Space Exploration by Ensemble Transfer Learning

Due to the increasing complexity of the processor architecture and the time-consuming software simulation, efficient design space exploration (DSE) has become a critical challenge in processor design. To address this challenge, recently machine learning techniques have been widely explored for predicting the performance of various configurations through conducting only a small number of simulations as the training samples. However, most existing methods randomly select some samples for simulation from the entire configuration space as training samples to build program-specific predictors. When a new program is considered, a large number of new program specific simulations are needed for building a new predictor. Thus considerable simulation cost is required for each program. In this paper, we propose an efficient cross-program DSE framework TrEE by combining a flexible statistical sampling strategy and ensemble transfer learning technique. Specifically, TrEE includes the following two phases which also form our major contributions: 1) proposing an orthogonal array based foldover design for flexibly sampling the representative configurations for simulation, and 2) proposing an ensemble transfer learning algorithm that can effectively transfer knowledge among different types of programs for improving the prediction performance for the new program. We evaluate the proposed TrEE on the benchmarks of SPEC CPU 2006 suite. The results demonstrate that TrEE is much more efficient and robust than state-of-art DSE techniques.