Green Computing

This comprehensive study analyzes the environmental impact of data centers specifically used for AI training and inference. The research provides detailed measurements of energy consumption and carbon emissions from major AI computing facilities. The authors present innovative solutions for reducing the environmental footprint of AI infrastructure, including advanced cooling systems, renewable energy integration, and workload optimization strategies. The paper also introduces new metrics for measuring and comparing the environmental efficiency of different AI computing architectures and deployment strategies.

This influential paper introduces the concept of Green AI, which encourages AI research that yields better results while consuming less computing power and thus lower environmental impact. The authors contrast Green AI with what they call Red AI: research that seeks to improve accuracy through massive computational power, regardless of the environmental cost. The paper proposes new evaluation criteria for AI research that include computational efficiency alongside accuracy, encouraging more sustainable approaches to AI development.

This research presents a comprehensive framework for developing environmentally sustainable software, with a particular focus on AI systems and applications. The study identifies key principles and practices for green software engineering, including energy-aware design patterns, efficient coding practices, and sustainability metrics. The authors provide concrete guidelines and case studies demonstrating how to implement sustainable software development practices throughout the entire software lifecycle.

This paper provides a comprehensive analysis of the environmental impact of AI systems throughout their lifecycle, from development to deployment and maintenance. The authors examine various strategies for reducing the carbon footprint of AI, including efficient model architectures, green computing practices, and renewable energy usage. The research also presents concrete recommendations for developing and deploying AI systems in an environmentally responsible manner.

This comprehensive survey examines the environmental impact of artificial intelligence throughout its lifecycle, from development to deployment and maintenance. The paper provides a systematic analysis of the challenges in making AI more sustainable, including hardware efficiency, algorithm design, and operational practices. The authors identify key opportunities for reducing AI’s environmental footprint and propose a research agenda for sustainable AI development.