The process of updating deep learning/AI models when they face new tasks or must accommodate changes in data can have significant costs in terms of computational resources and energy consumption.
The process of updating deep learning/AI models when they face new tasks or must accommodate changes in data can have significant costs in terms of computational resources and energy consumption. Researchers have developed a novel method that predicts those costs, allowing users to make informed decisions about when to update AI models to improve AI sustainability.
“There have been studies that focused on making deep learning model training more efficient,” says Jung-Eun Kim, corresponding author of a paper on the work and an assistant professor of computer science at North Carolina State University. “However, over a model’s life cycle, it will likely need to be updated many times. One reason is that, as our work here shows, retraining an existing model is much more cost effective than training a new model from scratch.
“If we want to address sustainability issues related to deep learning AI, we must look at computational and energy costs across a model’s entire life cycle – including the costs associated with updates. If you cannot predict what the costs will be ahead of time, it is impossible to engage in the type of planning that makes sustainability efforts possible. That makes our work here particularly valuable.”
Read More: North Carolina State University
