Researchers at North Carolina State University have developed a novel method to predict the computational and energy costs associated with updating deep learning artificial intelligence models. According to Jung-Eun Kim, assistant professor of computer science, this new technique called RESQUE allows users to compare the initial dataset used to train a model with the new dataset used for updates, estimating the costs required for retraining. This innovation has significant implications for AI sustainability as it enables informed decisions about when to update models and how to budget computational resources. Kim collaborated with graduate student Vishwesh Sangarya on the project, which will be presented at the Thirty-Ninth Association for the Advancement of Artificial Intelligence Conference. The research aims to make deep learning models more sustainable by reducing the need for frequent retraining from scratch, a process that demands substantial computational power and energy.
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