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.

翻译：人工智能（AI）经过数十年的发展，在技术与研究领域取得了显著进展，并被广泛应用于计算机视觉、自然语言处理、时间序列分析、语音合成等多个领域。在深度学习时代，特别是随着大型语言模型的出现，绝大多数研究者的注意力集中在追求最新最优（SOTA）结果上，导致模型规模和计算复杂度不断攀升。对高计算能力的需求不仅带来了更高的碳排放，也因限制了资金有限的中小型研究机构和公司参与研究而削弱了科研公平性。为应对AI对计算资源及环境影响带来的挑战，绿色计算已成为研究热点。本综述对绿色计算中使用的技术进行了系统性概述。我们提出了绿色计算的框架，并将其划分为四个关键组成部分：（1）绿色性度量，（2）节能型AI，（3）节能计算系统，以及（4）面向可持续发展的AI应用案例。针对每一部分，我们讨论了相关研究进展及优化AI效率的常用技术。我们得出结论，这一新兴研究方向有望解决资源约束与AI发展之间的矛盾。我们鼓励更多研究者关注该方向，推动AI更加环境友好。