MIT CSAIL aims for energy efficiency in AI model training

In a newly published paper, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers propose a system for training and running AI models in a way that's more environmentally friendly than previous approaches. They claim it can cut down on the pounds of carbon emissions involved to "low triple digits" in some cases, mainly by improving the computational efficiency of the aforementioned models. Impressive feats have been achieved with AI across domains like image synthesis, protein modeling, and autonomous driving, but the technology's sustainability issues remain largely unresolved. Last June, researchers at the University of Massachusetts at Amherst released a report estimating that the amount of power required for training and searching a certain model involves the emissions of roughly 626,000 pounds of carbon dioxide -- equivalent to nearly five times the lifetime emissions of the average U.S. car. The researchers' solution, a "once-for-all" network, trains a large model comprising many pretrained sub-models of different sizes that can be tailored to a range of platforms without retraining.