DeepEn2023: Energy Datasets for Edge Artificial Intelligence (Papers Track)
XIAOLONG TU (Georgia State University); Anik Mallik (University of North Carolina at Charlotte); Jiang Xie (University of North Carolina at Charlotte); Haoxin Wang (Georgia State University)
Climate change poses one of the most significant challenges to humanity. As a result of these climatic shifts, 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 U.S. $3.64 trillion. 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 enhance transparency regarding sustainability in on-device deep learning across a range of edge AI systems and applications.