“…Among the primary studies collected for this literature review on Green AI, only a small fraction ( 15 out of 98 papers ) makes tools available to tackle Green AI. The tools provided are of heterogeneous nature, and range from tools to monitor the resource efficiency of AI algorithms (Guldner et al, 2021), to tools optimizing the energy efficiency for stochastic edge inference (Kim & Wu, 2020), and implementations of convolutional neural networks optimized for energy efficiency (Mehta et al, 2019). Albeit numerous studies provide solution to tackle Green AI, only a fraction of them makes tools based on the solutions readily available online as an implemented tool.…”
With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this article, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm‐agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice.
This article is categorized under:
Technologies > Machine Learning
“…Among the primary studies collected for this literature review on Green AI, only a small fraction ( 15 out of 98 papers ) makes tools available to tackle Green AI. The tools provided are of heterogeneous nature, and range from tools to monitor the resource efficiency of AI algorithms (Guldner et al, 2021), to tools optimizing the energy efficiency for stochastic edge inference (Kim & Wu, 2020), and implementations of convolutional neural networks optimized for energy efficiency (Mehta et al, 2019). Albeit numerous studies provide solution to tackle Green AI, only a fraction of them makes tools based on the solutions readily available online as an implemented tool.…”
With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this article, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm‐agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice.
This article is categorized under:
Technologies > Machine Learning
“…In the physical tool and domain layers of solution architecture, choices in software development approaches can influence the energy consumed by software solutions, for example, through the use of energy-efficient programming techniques. 21 Power demands of software relate to the coding efficiency but also to the platform for which the code is developed, hosting and maintenance materials, and usability characteristics. The complete life cycle from “cradle to grave” of healthcare solutions should be considered when assessing environmental impacts.…”
Objective
The aim of this study was to develop a theory-based framework to enhance and accelerate development, selection, and implementation of solutions mitigating the climate impact of healthcare organizations.
Materials and Methods
Existing frameworks were combined to develop the Green-MIssion (Medical Informatics Solutions) framework. It was further developed and refined by mapping solutions from project plans and reviewing it with an expert panel.
Results
The framework classifies solutions into three categories: (1) monitor and measure environmental impact of a healthcare setting; (2) help create and increase awareness among employees and patients; and (3) interventions to reduce environmental impacts.
Discussion and Conclusion
The framework combines concepts from healthcare information technology and environmental sciences and can be used to structure green medical informatics solutions for different healthcare settings. Furthermore, research should evaluate its application for measuring and assessing the impact of green medical informatics solutions on environmental sustainability and climate resilience.
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