A High-accurate Multi-objective Exploration Framework for Design Space of CPU

To accelerate time-consuming multi-objective design space exploration of CPU, previous work trains prediction models using a set of performance metrics derived from few simulations, then predicts the rest. Unfortunately, the low accuracy of models limits the exploration effect, and how to achieve a good trade-off between multiple objectives is challenging. In this paper, we investigate various prediction models and find out the most accurate basic model. We enhance the model by ensemble learning to improve prediction accuracy. A hypervolume-improvement-based optimization method to trade off between multiple objectives is proposed together with a uniformity-aware selection algorithm to jump out of the local optimum. Experiments demonstrate that our open-source framework can reduce the distance to the Pareto optimal set by 76% and prediction error by 97% compared with the state-of-the-art work.