Abstract
The environmental impact of Artificial Intelligence (AI) is emerging as a significant global concern, particularly regarding model training. In this paper, we introduce GREEN (Guided Recommendations of Energy-Efficient Networks), a novel, inference-time approach for recommending Pareto-optimal AI model configurations that optimize validation performance and energy consumption across diverse AI domains and tasks. Our approach directly addresses the limitations of current eco-efficient neural architecture search methods, which are often restricted to specific architectures or tasks. Central to this work is EcoTaskSet, a dataset comprising training dynamics from over 1767 experiments across computer vision, natural language processing, and recommendation systems using both widely used and cutting-edge architectures. Leveraging this dataset and a prediction model, our approach demonstrates effectiveness in selecting the best model configuration based on user preferences. Experimental results show that our method successfully identifies energy-efficient configurations while ensuring competitive performance.
Abstract (translated)
人工智能（AI）对环境的影响正成为一个重要的全球性关注点，特别是在模型训练方面。本文介绍了GREEN（Guided Recommendations of Energy-Efficient Networks），这是一种新颖的推理时间方法，用于推荐帕累托最优的人工智能模型配置，以优化跨多种AI领域和任务的有效性和能耗。我们提出的方法直接解决了当前生态高效的神经架构搜索方法的局限性，这些方法通常局限于特定的架构或任务。本研究的核心是EcoTaskSet数据集，该数据集包含来自超过1767个实验的数据，涵盖了计算机视觉、自然语言处理以及推荐系统等领域的训练动态，并使用了广泛使用的和尖端的架构。通过利用此数据集和一个预测模型，我们的方法能够根据用户偏好有效地选择最佳模型配置。实验结果表明，我们的方法成功地识别出节能配置的同时还能保证竞争力的表现。
URL
https://arxiv.org/abs/2505.01468
