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

Авторы: Budennyy Semen, Lazarev Vladimir, Zakharenko Nikita, Korovin Alexey, Plosskaya Olga, Dimitrov Denis, Arkhipkin Vladimir, Oseledets Ivan, Barsola Ivan, Egorov Ilya, Kosterina Aleksandra, Zhukov Leonid
Журнал: ArXiv e-prints
Год издания: 2022
DOI: 10.48550/ARXIV.2208.00406
Аннотация: 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.
Добавил в систему: Буденный Семен Андреевич

	ИСТИНА	Войти в систему Регистрация
	ИСТИНА ИНХС РАН
	Главная Поиск Статистика О проекте Помощь

ИСТИНА