Multicore design space exploration via semi-supervised ensemble learning

With the increasing complexity of microprocessor architecture, the design space is growing exponentially and the software simulation technology is extremely time-consuming. Design space exploration becomes one major challenge when processors are designed. The paper proposed an efficient design space exploration method combining semi-supervised learning and ensemble learning techniques. Specifically, it includes two phases: uniform random sampling method is firstly employed to select a small set of representative design points, and then simulation is conducted with the points to constitute the training set; semi-supervised learning based AdaBoost (SSLBoost) model is further proposed to predict the responses of the configurations that have not been simulated. Then the optimal processor design configuration is found. The experimental results demonstrate that compared with the prediction models based on the existing artificial neural network and support vector machine (SVM), the proposed SSLBoost model can build a comparable accurate model using fewer simulations. When the number of simulation examples is fixed, the prediction accuracy of SSLBoost model is higher. © 2018, Editorial Board of JBUAA. All right reserved.