Cross prediction is an active research area. Many research works have used cross prediction to predict the target system's performance and power from the machine learning model trained on the source system. The source and target systems differ either in terms of instruction-set or hardware features. A widely used transfer learning technique utilizes the knowledge from a trained machine learning from one problem to predict targets in similar problems. In this work, we use transfer learning to achieve cross-system and cross-platform predictions. In cross-system prediction, we predict the physical system's performance (runtime) and power from the simulation systems dataset while predicting performance and the power for target system from source system both having different instruction-set in cross-platform prediction. We achieve runtime prediction accuracy of 90% and 80% and power prediction accuracy of 75% and 80% in cross-system and cross-platform predictions, respectively, for the best performing deep neural network model. Furthermore , we have evaluated the accuracy of univariate and multivariate machine learning models, the accuracy of compute-intensive and data-intensive applications, and the accuracy of the simulation and physical systems.
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.