Microarchitecture Power Modeling via Artificial Neural Network and Transfer Learning

Accurate and robust power models are highly demanded to explore better CPU designs. However, previous learning-based power models ignore the discrepancies in data distribution among different CPU designs, making it difficult to use data from the historical configuration to aid modeling for new target configuration. In this paper, we investigate the transferability of power models and propose a microarchitecture power modeling method based on transfer learning (TL). A novel TL method for artificial neural network (ANN)-based power models is proposed, where cross-domain mixup generates more auxiliary samples close to the target configuration to fill in the distribution discrepancy and domain-adversarial training extracts domain-invariant features to complete the target model construction. Experiments show that our method greatly improves the model transferability and can effectively utilize the knowledge of the existing CPU configuration to facilitate target power model construction.