Researchers show glare of energy consumption in the name of deep learning

Wait, what? Creating an AI can be way worse for the planet than a car? Think carbon footprint. That is what a group at the University of Massachusetts Amherst did. They set out to assess the energy consumption that is needed to train four large neural networks.

Deep learning involves processing very large amounts of data. In order to learn something as complex as language, the models have to be large. What price making models obtain gains in accuracy? Roping in exceptionally large computational resources to do so is the price, causing substantial energy consumption.

Researchers reported their findings, that "the process can emit more than 626,000 pounds of carbon dioxide equivalent—nearly five times the lifetime emissions of the average American car (and that includes manufacture of the car itself)."

These models are costly to train and develop—-costly in the financial sense due to the cost of hardware and electricity or cloud compute time, and costly in the environmental sense. The environmental cost is due to the carbon footprint. The paper sought to bring this issue to the attention of NLP researchers "by quantifying the approximate financial and environmental costs of training a variety of recently successful neural network models for NLP."

Read more at Tech Xplore

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