能源消耗
推论
人工智能
计算机科学
人工神经网络
跟踪(教育)
消费(社会学)
深度学习
能量(信号处理)
高效能源利用
机器学习
持续性
环境经济学
工业工程
工程类
经济
数学
社会学
社会科学
生物
统计
电气工程
教育学
生态学
作者
Semen Budennyy,Vladimir Lazarev,Nikita Zakharenko,Alexey V. Korovin,Olga Plosskaya,Denis Dimitrov,Vladimir Arkhipkin,Ivan Oseledets,Ivan Barsola,Ilya N. Egorov,Aleksandra Kosterina,Leonid Zhukov
出处
期刊:Cornell University - arXiv
日期:2022-07-31
标识
DOI:10.48550/arxiv.2208.00406
摘要
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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