  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.
