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Measuring AI's Carbon Footprint

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In a preliminary experiment, the team found that a server’s GPUs used 74% of its energy, so the team focused on GPU usage.

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Machine-learning models are growing exponentially larger. At the same time, they require exponentially more energy to train, so that they can accurately process images or text or video. As the AI community grapples with its environmental impact, some conferences now ask paper submitters to include information on CO2 emissions. New research offers a more accurate method for calculating those emissions. It also compares factors that affect them, and tests two methods for reducing them.

Several software packages estimate the carbon emissions of AI workloads. Recently a team at Université Paris-Saclay tested a group of these tools to see if they were reliable. "And they're not reliable in all contexts," says Anne-Laure Ligozat, a co-author of that study who was not involved in the new work.

The new approach differs in two respects, says Jesse Dodge, a research scientist at the Allen Institute for AI and the lead author of the new paper, which he presented last week at the ACM Conference on Fairness, Accountability, and Transparency (FAccT). First, it records server chips' energy usage as a series of measurements, rather than summing their use over the course of training. Second, it aligns this usage data with a series of data points indicating the local emissions per kilowatt-hour (kWh) of energy used. This number also changes continually. "Previous work doesn't capture a lot of the nuance there," Dodge says.

From IEEE Spectrum
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