Climate change poses one of the most significant challenges to humanity. As a result of these climatic changes, the frequency of weather, climate, and water-related disasters has multiplied fivefold over the past 50 years, resulting in over 2 million deaths and losses exceeding $3.64 trillion USD. Leveraging AI-powered technologies for sustainable development and combating climate change is a promising avenue. Numerous significant publications are dedicated to using AI to improve renewable energy forecasting, enhance waste management, and monitor environmental changes in real time. However, very few research studies focus on making AI itself environmentally sustainable. This oversight regarding the sustainability of AI within the field might be attributed to a mindset gap and the absence of comprehensive energy datasets. In addition, with the ubiquity of edge AI systems and applications, especially on-device learning, there is a pressing need to measure, analyze, and optimize their environmental sustainability, such as energy efficiency. To this end, in this paper, we propose large-scale energy datasets for edge AI, named DeepEn2023, covering a wide range of kernels, state-of-the-art deep neural network models, and popular edge AI applications. We anticipate that DeepEn2023 will improve transparency in sustainability in on-device deep learning across a range of edge AI systems and applications. For more information, including access to the dataset and code, please visit https://amai-gsu.github.io/DeepEn2023.

翻译：气候变化是人类面临的最严峻挑战之一。过去50年间，天气、气候及水相关灾害的发生频率已激增至原先的五倍，造成逾200万人死亡及超过3.64万亿美元的经济损失。利用人工智能技术推动可持续发展、应对气候变化是一条极具前景的路径。众多重要研究成果致力于运用AI提升可再生能源预测、优化废弃物管理及实时监测环境变化。然而，鲜有研究聚焦于AI自身的环境可持续性。该领域对AI可持续性的忽视可能源于认知差距及综合性能耗数据集的缺失。此外，随着边缘AI系统及应用的普及（尤其是设备端学习），亟需对其环境可持续性（如能效）进行测量、分析与优化。为此，本文提出名为DeepEn2023的大规模边缘AI能耗数据集，涵盖广泛的内核、最新深度神经网络模型及主流边缘AI应用。我们预期DeepEn2023将提升各类边缘AI系统及应用在设备端深度学习可持续性方面的透明度。更多信息（包括数据集与代码获取）请访问https://amai-gsu.github.io/DeepEn2023。