Alleviating Dataset Constraints through Synthetic Data Generation in Machine Learning Driven Power Modeling

In recent years, Machine learning (ML) methods have emerged as a promising approach for mitigating the challenges with software power measurement and estimation. While ML-driven methods have been effective in advancing the state of the art in energy-efficient computing, their predictive abilities are often limited by the quality and the size of the training datasets. This paper presents a diffusion-based generative method for synthesizing software energy data to create larger representative datasets for power modeling and related tasks. We demonstrate the effectiveness of our methods in the context of energy-efficient graph analytics. We use an augmented dataset to train a regression model and a classifier to predict the GPU power consumption of graph applications running on different classes of input graphs. Experimental evaluation shows the accuracy of the predictive models can be improved by as much as 11% when trained on the augmented dataset than over the original dataset.