Sustainable/_Index

This comprehensive survey examines various approaches to make the training of large language models more efficient and environmentally sustainable. The research analyzes different techniques including model compression, efficient attention mechanisms, and hardware-aware training strategies that can significantly reduce the computational and energy costs. The authors provide a systematic comparison of different efficiency methods and their impact on model performance, training time, and energy consumption.

This comprehensive survey examines various approaches to making transformer models more computationally efficient and environmentally sustainable. The research analyzes different architectural innovations and training strategies that reduce the computational and energy requirements of transformer models while maintaining their effectiveness. The authors provide a systematic comparison of different efficiency techniques and their impact on model performance, training costs, and environmental footprint.

This comprehensive study explores methods for developing energy-efficient vision transformers while maintaining high performance in computer vision tasks. The research evaluates various optimization techniques including architecture modifications, training strategies, and inference optimizations specifically designed for vision transformers. The authors demonstrate significant reductions in computational costs and energy consumption while preserving model accuracy across different vision tasks.

This pioneering study examines the carbon footprint of training natural language processing models. The authors quantify the financial and environmental costs of training various NLP models. The study reveals that training a single BERT model can emit as much CO2 as a trans-Atlantic flight, and that the computational costs of NLP models double every 3-4 months. The authors provide concrete recommendations to reduce environmental impact, particularly by prioritizing energy efficiency in model design and using renewable energy sources for training.

This comprehensive review examines state-of-the-art approaches for making deep learning more energy-efficient across the entire stack, from hardware to algorithms. The research analyzes various efficiency techniques including model compression, neural architecture search, and hardware-software co-design for energy-efficient deep learning. The authors provide detailed case studies and empirical evaluations of different approaches, offering insights into their effectiveness for reducing energy consumption while maintaining model performance.

L’enquête annuelle “Pour un numérique soutenable” de l’Arcep analyse les enjeux environnementaux liés à la transformation numérique en France. Cette édition 2025 présente des données actualisées sur l’impact environnemental des infrastructures numériques, des terminaux et des usages, ainsi que les initiatives du secteur pour réduire son empreinte écologique.

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.

Cette étude porte sur les réserves rentables mondiales de métaux, et notamment ceux utilisés pour fabriquer nos équipements numériques et high-tech. Les pays occidentaux sont de plus en plus dépendants des hautes technologies (high-tech). Or, les « stocks » de minerais permettant de fabriquer ces technologies, dont le numérique et l’intelligence artificielle, semblent se dégrader.

This paper bridges the gap between AI ethics and environmental sustainability, proposing a framework that considers both ethical and environmental implications of AI development. The research examines how ethical AI principles can be aligned with environmental sustainability goals, addressing issues such as computational efficiency, resource allocation, and environmental justice. The authors propose concrete guidelines for developing AI systems that are both ethically sound and environmentally sustainable.

The last decade saw the acceleration of new technologies adoption, shaping the digital landscape in terms of speed, quality and connectivity for multimedia contents and communication tools. While many activities have been able to benefit from the numerous innovations (4.0 industry, e-commerce, telecommunications, etc.) to develop, this growth has always been coupled with a significant increase of pressures on the environment and natural resources. The French Agency for Ecological Transition (ADEME) has proposed four scenarios for achieving carbon neutrality by 2050 in its study “Transition 2050, Choisir maintenant, Agir pour le Climat”. They aim to link the technical and economic dimensions with thoughts on the transformations in society that they imply or that they give rise to. This is the context for the study entitled “Assessment of the environmental impact of digital technology in France and prospective analysis”. The present report on the “prospective analysis for 2030 and 2050 and medium- and long-term courses of action”, is a continuation of Task 1 on the “state of play and courses of action” and Task 2 on the “environmental assessment of digital services in France”. Specifically, Task 3 aims to assess the environmental impact of the digital sector in France for the 2030 and 2050 timeframes according to a trend scenario and different scenarios for the mitigation of this impact. Like Task 2, it consists of an evaluation of the impacts using the Life Cycle Assessment (LCA) methodology. This methodology focuses on the three thirds of the digital world: user terminals, networks and data centers. The results are presented at the France-wide scale and are detailed under several levels of analysis in order to get a more acute interpretation and a better comprehension of direct environmental stakes related to digital technologies in France.