CPU Power Modeling Through Training Data Selection

CPU power modeling aims to address issues related to power prediction and fine-grained power measurement during power management. However, existing studies have mainly focused on designing supervised learning methods for power modeling, neglecting the effect of training samples. Therefore, this paper presents a CPU power modeling method that adaptively selects high-quality sample data iteratively. The method is applied to linear regression (LR), neural networks (NN) and random forest models (RF) on x86 and ARM-based platforms. Experimental results show that the method can reduce the average MAPE by 13.33% for the LR model, 27.45% for the NN model and 28.30% for the RF model.