# 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  
**arXiv:** [2311.00447](https://arxiv.org/abs/2311.00447)  
**Date:** 2023-11-01  
**Summary page:** https://www.summarizepaper.com/en/paper/2311.00447v1/

## Key points

- Artificial Intelligence (AI) has advanced significantly in technology and research
- Revolutionized fields such as computer vision, natural language processing, time-series analysis, and speech synthesis
- Focus on achieving new state-of-the-art results through deep learning and Large Language Models
- Escalation in model size and computational complexity leading to increased carbon emissions
- Green Computing emerging as a pivotal research area to address environmental challenges in AI
- Framework proposed by researchers includes Measures of Greenness, Energy-Efficient AI, Energy-Efficient Computing Systems, and AI Use Cases for Sustainability
- Techniques aimed at optimizing AI efficiency while minimizing environmental impact are being explored
- Emphasis on making AI more environmentally friendly through embracing Green Computing principles for a greener future

## Summary

Artificial Intelligence (AI) has made significant strides in technology and research over the past few decades. It has revolutionized various fields such as computer vision, natural language processing, time-series analysis, and speech synthesis. However, with the advent of deep learning and the emergence of Large Language Models, there has been a predominant focus on achieving new state-of-the-art results. This has led to an escalation in model size and computational complexity. This quest for high computing power has resulted in increased carbon emissions and created barriers for smaller research institutions and companies with limited resources to actively participate in AI research. To address these challenges, Green Computing has emerged as a pivotal research area. In a recent survey conducted by You Zhou et al., they provide a comprehensive overview of the technologies employed in Green Computing. The framework proposed by the researchers delineates four key components: Measures of Greenness, Energy-Efficient AI, Energy-Efficient Computing Systems, and AI Use Cases for Sustainability. The survey delves into the progress made in each component and explores commonly used techniques aimed at optimizing AI efficiency while minimizing environmental impact. The researchers emphasize that this new direction in AI research holds immense potential to reconcile resource constraints with continued advancements in AI development. They urge fellow researchers to shift their focus towards making AI more environmentally friendly by embracing Green Computing principles. The study was authored by You Zhou, Xiujing Lin, Xiang Zhang, Maolin Wang, Gangwei Jiang,
Huakang Lu,Yupeng Wu,Kai Zhang,Zhe Yang from Victor Research Institute. Their collaborative effort underscores the importance of prioritizing sustainability in AI innovation to ensure a greener future for technological advancements.