부산대학교 도서관

To accelerate time-consuming multiobjective design space exploration of CPU microarchitecture, previous work trains prediction models using a set of performance metrics derived from a few simulations, then predicts the rest. Unfortunately, the low accuracy of models limits the exploration effect, and how to achieve a good tradeoff between multiple objectives while reducing exploration time is challenging. In this article, we investigate various prediction models and find out the most accurate basic model. We enhance the model by ensemble learning and generate Pareto-rank-based sample weights to improve prediction accuracy. A hypervolume-improvement-based optimization method to tradeoff between multiple objectives is proposed together with a uniformity-aware selection algorithm to jump out of the local optimum. Furthermore, the exploration time is reduced owing to a proposed Pareto-aware filter algorithm. Experiments demonstrate that our open-source framework can reduce the distance to the Pareto-optimal set by 39% compared with the state-of-the-art framework.