Play it Straight: An Intelligent Data Pruning Technique for Green-AI

The escalating climate crisis demands urgent action to mitigate the environmental impact of energy-intensive technologies, including Artificial Intelligence (AI). Lowering AI’s environmental impact requires adopting energy-efficient approaches for training Deep Neural Networks (DNNs). One such approach is to use Dataset Pruning (DP) methods to reduce the number of training instances, and thus the total energy consumed. Numerous DP methods have been proposed in the literature (e.g., GraNd and Craig), with the ultimate aim of speeding up model training. On the other hand, Active Learning (AL) approaches, originally conceived to repeatedly select the best data to be labeled by a human expert (from a large collection of unlabeled data), can be exploited as well to train a model on a relatively small subset of (informative) examples. However, despite allowing for reducing the total amount of training data, most DP methods and pure AL-based schemes entail costly computations that may strongly limit their energy saving potential. In this work, we empirically study the effectiveness of DP and AL methods in curbing energy consumption in DNN training, and propose a novel approach to DNN learning, named Play it straight, which efficiently combines data selection methods and AL-like incremental training. Play it straight is shown to outperform traditional DP and AL approaches, achieving a better trade-off between accuracy and energy efficiency.