Abstract:Deep Neural Networks (DNNs) have served as a catalyst in introducing a plethora of next-generation services in the era of Internet of Things (IoT), thanks to the availability of massive amounts of data collected by the objects on the edge. Currently, DNN models are used to deliver many Artificial Intelligence (AI) services that include image and natural language processing, speech recognition, and robotics. Accordingly, such services utilize various DNN models that make it computationally intensive for deploym… Show more
“…The majority of paper results adopt laboratory experiments ( 73 out of 98 papers ), while only a fraction uses other research strategies, such as field experiments ( 6 out of 98 papers ), that is, experiments conducted in pre‐existing settings and computer simulations , that is, “in silico” simulations conducted in a nonempirical setting ( 5 out of 98 papers ). As examples, Liu et al (2019) use a field study to assess a green software stack for computer vision of autonomous robots, while Yosuf et al (2021) leverage computer simulations to study how virtualized cloud fog networks can be used to improve AI energy efficiency. The 12 papers not displaying any research strategy correspond to the position papers (cf., the “None” category in Figure 11).…”
Section: Resultsmentioning
confidence: 99%
“…Out of all Green AI strategies, among the ones which report concrete saving percentages, a technique based on structure simplification for deep neural networks results to save more energy, amounting to 115% energy savings (Zhang et al, 2018a). The other techniques which result to optimize energy the most are based on quantizing the inputs of decision trees (Abreu et al, 2020) (97% energy savings), using data‐centric Green AI techniques (Verdecchia et al, 2022b) (92% energy savings), and leveraging efficient deployment of AI algorithms via virtualized cloud fog networks (91% energy savings) (Yosuf et al, 2021). Overall, more than half of the papers explicitly reporting energy saving percentages report a saving of at least 50% ( 17 out of 27 papers ), while only a minor number savings between 13% and 49%.Studies report energy savings between 13% and 115% energy savings, with more than half of the papers reporting savings of at least 50%.…”
Section: Resultsmentioning
confidence: 99%
“…Out of all Green AI strategies, among the ones which report concrete saving percentages, a technique based on structure simplification for deep neural networks results to save more energy, amounting to 115% energy savings (Zhang et al, 2018a). The other techniques which result to optimize energy the most are based on quantizing the inputs of decision trees (Abreu et al, 2020) (97% energy savings), using data-centric Green AI techniques (Verdecchia et al, 2022b) (92% energy savings), and leveraging efficient deployment of AI algorithms via virtualized cloud fog networks (91% energy savings) (Yosuf et al, 2021). Overall, more…”
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
“…The majority of paper results adopt laboratory experiments ( 73 out of 98 papers ), while only a fraction uses other research strategies, such as field experiments ( 6 out of 98 papers ), that is, experiments conducted in pre‐existing settings and computer simulations , that is, “in silico” simulations conducted in a nonempirical setting ( 5 out of 98 papers ). As examples, Liu et al (2019) use a field study to assess a green software stack for computer vision of autonomous robots, while Yosuf et al (2021) leverage computer simulations to study how virtualized cloud fog networks can be used to improve AI energy efficiency. The 12 papers not displaying any research strategy correspond to the position papers (cf., the “None” category in Figure 11).…”
Section: Resultsmentioning
confidence: 99%
“…Out of all Green AI strategies, among the ones which report concrete saving percentages, a technique based on structure simplification for deep neural networks results to save more energy, amounting to 115% energy savings (Zhang et al, 2018a). The other techniques which result to optimize energy the most are based on quantizing the inputs of decision trees (Abreu et al, 2020) (97% energy savings), using data‐centric Green AI techniques (Verdecchia et al, 2022b) (92% energy savings), and leveraging efficient deployment of AI algorithms via virtualized cloud fog networks (91% energy savings) (Yosuf et al, 2021). Overall, more than half of the papers explicitly reporting energy saving percentages report a saving of at least 50% ( 17 out of 27 papers ), while only a minor number savings between 13% and 49%.Studies report energy savings between 13% and 115% energy savings, with more than half of the papers reporting savings of at least 50%.…”
Section: Resultsmentioning
confidence: 99%
“…Out of all Green AI strategies, among the ones which report concrete saving percentages, a technique based on structure simplification for deep neural networks results to save more energy, amounting to 115% energy savings (Zhang et al, 2018a). The other techniques which result to optimize energy the most are based on quantizing the inputs of decision trees (Abreu et al, 2020) (97% energy savings), using data-centric Green AI techniques (Verdecchia et al, 2022b) (92% energy savings), and leveraging efficient deployment of AI algorithms via virtualized cloud fog networks (91% energy savings) (Yosuf et al, 2021). Overall, more…”
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
“…Cloud computing can enhance the scalability and reliability of IoT-enabled energy management systems. By providing on-demand access to computing resources and storage, cloud computing can help to process and analyze the large amounts of data generated by IoT devices, enabling more effective energy management [14]. Additionally, cloud computing can help improve energy management systems' reliability and resilience by providing redundant storage and computing resources (Figure 6).…”
The Internet of Things (IoT) has the potential to revolutionize energy management by enabling the collection and analysis of real-time data from various energy sources. This research paper investigates the impact of the Internet of Things (IoT) on energy management. The paper provides an overview of IoT and its potential applications in energy management, including improved efficiency, reduced costs, and better resource utilization. The benefits of using IoT for energy management and the major challenges that may arise in implementing IoT-enabled energy management are discussed. Potential solutions to these challenges, such as artificial intelligence and cloud computing, are presented, along with case studies of IoT-enabled energy management in different industries. The paper also analyzes the impact of IoT on energy efficiency in telecommunications and cloud infrastructure. Finally, the future outlook for IoT and energy management is discussed, including potential developments in edge computing, advanced analytics, and 5G networks. Overall, this paper highlights the potential of IoT to revolutionize energy management and provides insights into the challenges and opportunities of implementing IoT-enabled energy management solutions.
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