Cross-Platform Performance Prediction with Transfer Learning using Machine Learning

Machine-learning models are widely used for performance prediction due to its applications in the advancements of hardware-software co-development. Several researchers have focused on predicting the performance of an unknown target platform (or system) from the known performance of a particular platform (or system); we call this as the cross-platform prediction. Transfer learning is used to reuse previously gained knowledge on a similar task. In this paper, we use transfer learning for solving two problems cross-platform prediction and cross-systems prediction. Our result shows the prediction error of 15% in case of cross-systems (Simulated to Physical) prediction whereas in case of the cross-platform prediction error of 17% for simulation-based X86 to ARM prediction and 23% for physical Intel Core to Intel-Xeon system using best performing tree-based machine-learning model. We have also experimented with dimensionality reduction using PCA and selection of best hyper-parameters using grid search techniques.