Researchers show how AI’s energy consumption can be greatly reduced with energy-efficient models, leading to substantial energy savings without significantly affecting performance. They provide a guide for AI development prioritizing energy efficiency. Credit: SciTechDaily.com
But the new study, of which he and computer science student Pedram Bakhtiarifard are two of the authors, demonstrates that it is easy to curb a great deal of COe without compromising the precision of an AI model. Doing so demands keeping climate costs in mind from the design and training phases of AI models.
Based on the calculations, the researchers present a benchmark collection of AI models that use less energy to solve a given task, but which perform at approximately the same level. The study shows that by opting for other types of models or by adjusting models, 70-80% energy savings can be achieved during the training and deployment phase, with only a 1% or less decrease in performance. And according to the researchers, this is a conservative estimate.
“Oftentimes, many models are trained before finding the one that is suspected of being the most suitable for solving a particular task. This makes the development of AI extremely energy-intensive. Therefore, it would be more climate-friendly to choose the right model from the outset, while choosing one that does not consume too much power during the training phase.”
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