mlsanityData to 7 Oct 2026

On the Opportunities of Green Computing: A Survey

arXiv 2311.00447 ↗First posted 1 Nov 2023cs.AIcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 100 of its 737 references that are in the map. First posted on arXiv 1 Nov 2023.

Builds on

100 of 737 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 0; 2026: 1.

7 papers.

  1. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  2. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  3. 2024Large Language Model Distilling Medication Recommendation Modelcited by 25
  4. 2024FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clusteringcited by 5
  5. 2023How to Bridge the Gap between Modalities: Survey on Multimodal Large Language Modelcited by 3
  6. 2024Insights into resource utilization of code small language models serving with runtime engines and execution providerscited by 0
  7. 2026Towards Sustainable Artificial Intelligence: A Comprehensive Review and Comparative Analysis of Deep Learning Models' Carbon Footprintcited by 0