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arXiv · 2208.00406

Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI

Semen Budennyy·Vladimir Lazarev·Nikita Zakharenko·Alexey Korovin·Olga Plosskaya·Denis Dimitrov·Vladimir Arkhipkin·Ivan Oseledets·Ivan Barsola·Ilya Egorov·Aleksandra Kosterina·Leonid Zhukov
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Abstract

The size and complexity of deep neural networks continue to grow exponentially, significantly increasing energy consumption for training and inference by these models. We introduce an open-source package eco2AI to help data scientists and researchers to track energy consumption and equivalent CO2 emissions of their models in a straightforward way. In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting. We encourage research community to search for new optimal Artificial Intelligence (AI) architectures with a lower computational cost. The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.

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Semen Budennyy, Vladimir Lazarev, Nikita Zakharenko, Alexey Korovin, Olga Plosskaya, Denis Dimitrov, Vladimir Arkhipkin, Ivan Oseledets, Ivan Barsola, Ilya Egorov, Aleksandra Kosterina, Leonid Zhukov. 2022-07-31. Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI. https://arxiv.org/abs/2208.00406

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