On the Opportunities of Green Computing: A Survey
- URL: http://arxiv.org/abs/2311.00447v3
- Date: Thu, 9 Nov 2023 03:08:34 GMT
- Title: On the Opportunities of Green Computing: A Survey
- Authors: You Zhou, Xiujing Lin, Xiang Zhang, Maolin Wang, Gangwei Jiang,
Huakang Lu, Yupeng Wu, Kai Zhang, Zhe Yang, Kehang Wang, Yongduo Sui, Fengwei
Jia, Zuoli Tang, Yao Zhao, Hongxuan Zhang, Tiannuo Yang, Weibo Chen, Yunong
Mao, Yi Li, De Bao, Yu Li, Hongrui Liao, Ting Liu, Jingwen Liu, Jinchi Guo,
Xiangyu Zhao, Ying WEI, Hong Qian, Qi Liu, Xiang Wang, Wai Kin (Victor) Chan,
Chenliang Li, Yusen Li, Shiyu Yang, Jining Yan, Chao Mou, Shuai Han, Wuxia
Jin, Guannan Zhang and Xiaodong Zeng
- Abstract summary: Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
- Score: 80.21955522431168
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial Intelligence (AI) has achieved significant advancements in
technology and research with the development over several decades, and is
widely used in many areas including computing vision, natural language
processing, time-series analysis, speech synthesis, etc. During the age of deep
learning, especially with the arise of Large Language Models, a large majority
of researchers' attention is paid on pursuing new state-of-the-art (SOTA)
results, resulting in ever increasing of model size and computational
complexity. The needs for high computing power brings higher carbon emission
and undermines research fairness by preventing small or medium-sized research
institutions and companies with limited funding in participating in research.
To tackle the challenges of computing resources and environmental impact of AI,
Green Computing has become a hot research topic. In this survey, we give a
systematic overview of the technologies used in Green Computing. We propose the
framework of Green Computing and devide it into four key components: (1)
Measures of Greenness, (2) Energy-Efficient AI, (3) Energy-Efficient Computing
Systems and (4) AI Use Cases for Sustainability. For each components, we
discuss the research progress made and the commonly used techniques to optimize
the AI efficiency. We conclude that this new research direction has the
potential to address the conflicts between resource constraints and AI
development. We encourage more researchers to put attention on this direction
and make AI more environmental friendly.
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