Energy Efficient AI

Energy-efficient AI focuses on minimizing the environmental and economic costs associated with training and deploying artificial intelligence models, primarily by reducing energy consumption. Current research emphasizes developing lightweight model architectures (like EEGNet), optimizing training algorithms (exploring alternatives to SGD), and employing data-centric approaches such as elite sample selection and dataset modification to reduce training data size and computational complexity. These efforts are crucial for making AI more sustainable and accessible, particularly for resource-constrained applications like wearable devices and mobile health, while also addressing the growing concerns about the carbon footprint of large-scale AI deployments.