DeepEn2023: Energy Datasets for Edge Artificial Intelligence
- URL: http://arxiv.org/abs/2312.00103v1
- Date: Thu, 30 Nov 2023 16:54:36 GMT
- Title: DeepEn2023: Energy Datasets for Edge Artificial Intelligence
- Authors: Xiaolong Tu, Anik Mallik, Haoxin Wang, Jiang Xie
- Abstract summary: 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.
- Score: 3.0996501197166975
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate [69.05573887799203]
Much of this debate has concentrated on direct impact without addressing the significant indirect effects.
This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption.
We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses.
arXiv Detail & Related papers (2025-01-27T22:45:06Z) - Exploring the sustainable scaling of AI dilemma: A projective study of corporations' AI environmental impacts [0.0]
We propose a methodology to estimate the environmental impact of a company's AI portfolio.
Results confirm that large generative AI models consume up to 4600x more energy than traditional models.
Mitigating the environmental impact of Generative AI by 2030 requires coordinated efforts across the AI value chain.
arXiv Detail & Related papers (2025-01-24T08:58:49Z) - Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI [67.58673784790375]
We argue that the 'bigger is better' AI paradigm is not only fragile scientifically, but comes with undesirable consequences.
First, it is not sustainable, as its compute demands increase faster than model performance, leading to unreasonable economic requirements and a disproportionate environmental footprint.
Second, it implies focusing on certain problems at the expense of others, leaving aside important applications, e.g. health, education, or the climate.
arXiv Detail & Related papers (2024-09-21T14:43:54Z) - Artificial Intelligence Approaches for Energy Efficiency: A Review [0.0]
United Nations set Sustainable Development Goals and this paper focuses on 7th (Affordable and Clean Energy), 9th (Industries, Innovation and Infrastructure), and 13th (Climate Action) goals.
arXiv Detail & Related papers (2024-07-31T16:24:52Z) - Towards A Comprehensive Assessment of AI's Environmental Impact [0.5982922468400899]
Recent surge of interest in machine learning has sparked a trend towards large-scale adoption of AI/ML.
There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle.
This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations.
arXiv Detail & Related papers (2024-05-22T21:19:35Z) - Green Edge AI: A Contemporary Survey [46.11332733210337]
The transformative power of AI is derived from the utilization of deep neural networks (DNNs)
Deep learning (DL) is increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs)
Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL.
arXiv Detail & Related papers (2023-12-01T04:04:37Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - Towards Sustainable Artificial Intelligence: An Overview of
Environmental Protection Uses and Issues [0.0]
This paper describes the paradox of an energy-consuming technology serving the ecological challenges of tomorrow.
It draws on numerous examples from AI for Green players to present use cases and concrete examples.
The environmental dimension is part of the broader ethical problem of AI, and addressing it is crucial for ensuring the sustainability of AI in the long term.
arXiv Detail & Related papers (2022-12-22T14:31:48Z) - Eco2AI: carbon emissions tracking of machine learning models as the
first step towards sustainable AI [47.130004596434816]
In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting.
The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.
arXiv Detail & Related papers (2022-07-31T09:34:53Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.