A Systematic Review of Green AI

Verdecchia, Roberto; Sallou, June; Cruz, Luis J.

doi:10.48550/arxiv.2301.11047

Cited by 3 publications

(2 citation statements)

References 36 publications

(49 reference statements)

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“…Green CV can be understood as a specific application of both "green artificial intelligence" and "sustainable IT" within the computer vision field. Here are some principles that we have advanced for developing environmentally friendly computer vision systems, which we have derived from a survey of the literature on Green Computing, 19 Green AI, 22 and Sustainable Computing: 17 • Efficient Model Design: Build lightweight neural networks that require less energy. This involves prioritizing smaller models with similar performance or using techniques like quantization and pruning to streamline existing models.…”

Section: Key Principles For Developing Environmentally Friendly Compu...mentioning

confidence: 99%

Advancing green computer vision: principles and practices for sustainable development for real-time computer vision applications

Kramer,

Roth

2024

Real-Time Processing of Image, Depth, and Video Information 2024

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Recent algorithmic developments, specifically in deep learning, have propelled computer vision forward for practical applications. However, the high computational complexity and the resulting power consumption are often overlooked issues. This is not only a problem if the systems need to be installed in the wild, where often only a limited electricity supply is available, but also in the context of high energy consumption. To address both aspects, we explore the intersection of green artificial intelligence and real-time computer vision, focusing on the use of single-board computers. To this end, we need to take into account the limitations of single-board computers, including limited processing power and storage capacity, and demonstrate how the algorithm and data optimization ensure high-quality results, however, at a drastically reduced computational effort. Energy efficiency can be increased, aligning with the goals of Green AI and making such systems less dependent on a permanent electrical power supply.

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Section: Key Principles For Developing Environmentally Friendly Compu...mentioning

confidence: 99%

Advancing green computer vision: principles and practices for sustainable development for real-time computer vision applications

Kramer,

Roth

2024

Real-Time Processing of Image, Depth, and Video Information 2024

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“…In some cases, small reductions in accuracy (< 0.1) can reduce energy consumption by more than 70%. For an overview of publications addressing Green AI (AI systems developed with sustainability and costs considered), we refer to the systematic review by Verdecchia et al [31].…”

Section: B Energy Consumption Of Machine Learning Modelsmentioning

confidence: 99%

An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code

Hort,

Grishina,

Moonen

2023

2023 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Large language models trained on source code can support a variety of software development tasks, such as code recommendation and program repair. Large amounts of data for training such models benefit the models' performance. However, the size of the data and models results in long training times and high energy consumption. While publishing source code allows for replicability, users need to repeat the expensive training process if models are not shared.GOALS: The main goal of the study is to investigate if publications that trained language models for software engineering (SE) tasks share source code and trained artifacts. The second goal is to analyze the transparency on training energy usage.METHODS: We perform a snowballing-based literature search to find publications on language models for source code, and analyze their reusability from a sustainability standpoint.RESULTS: From a total of 494 unique publications, we identified 293 relevant publications that use language models to address code-related tasks. Among them, 27% (79 out of 293) make artifacts available for reuse. This can be in the form of tools or IDE plugins designed for specific tasks or task-agnostic models that can be fine-tuned for a variety of downstream tasks. Moreover, we collect insights on the hardware used for model training, as well as training time, which together determine the energy consumption of the development process.CONCLUSION: We find that there are deficiencies in the sharing of information and artifacts for current studies on source code models for software engineering tasks, with 40% of the surveyed papers not sharing source code or trained artifacts. We recommend the sharing of source code as well as trained artifacts, to enable sustainable reproducibility. Moreover, comprehensive information on training times and hardware configurations should be shared for transparency on a model's carbon footprint.

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Marigold: A machine learning-based web app for zebrafish pose tracking

Teicher,

Madison Riffe,

Barnaby

et al. 2024

Preprint

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High-throughput behavioral analysis is important for drug discovery, toxicological studies, and modeling neurological disorders such as autism and epilepsy. Zebrafish embryos and larvae are ideal for such applications because they are spawned in large clutches, develop rapidly, feature a relatively simple nervous system, and have orthologs to many human disease genes. However, existing software for video-based behavioral analysis can be incompatible with recordings that contain dynamic backgrounds or foreign objects, lack support for multiwell formats, require expensive hardware, and/or demand considerable programming expertise. Here, we introduce Marigold, a free and open source web app for high-throughput behavioral analysis of embryonic and larval zebrafish. Marigold features an intuitive graphical user interface (GUI), tracks up to 10 user-defined keypoints, supports both single- and multiwell formats, and exports a range of kinematic parameters in addition to publication-quality data visualizations. By leveraging a highly efficient, custom-designed neural network architecture, Marigold achieves competitive training and inference speeds even on modestly powered computers lacking a discrete graphics processing unit (GPU). As a web app, Marigold does not require any installation and runs within popular web browsers on ChromeOS, Linux, macOS, and Windows. To demonstrate Marigold's utility, we used two sets of experiments. First, we examined novel aspects of the touch-evoked escape response in techno trousers (tnt) mutant embryos, which contain a previously described loss-of-function mutation in the gene encoding Eaat2b, a glial glutamate transporter. We identified differences and interactions between touch location (head vs. tail) and genotype. Second, we investigated the effects of feeding on larval visuomotor behavior at 5 and 7 days post-fertilization (dpf). We found differences in the number and vigor of swimming bouts between fed and unfed fish at both time points, as well as interactions between developmental stage and feeding regimen. In both biological experiments presented here, the use of Marigold facilitated novel behavioral findings. Marigold's ease of use, robust pose tracking, amenability to diverse experimental paradigms, and flexibility regarding hardware requirements make it a powerful tool for analyzing zebrafish behavior, especially in low-resource settings such as course-based undergraduate research experiences (CUREs). Marigold is available at: <a href="https://downeslab.github.io/marigold/">https://downeslab.github.io/marigold/</a>.

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A Systematic Review of Green AI

Cited by 3 publications

References 36 publications

Advancing green computer vision: principles and practices for sustainable development for real-time computer vision applications

Advancing green computer vision: principles and practices for sustainable development for real-time computer vision applications

An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code

Marigold: A machine learning-based web app for zebrafish pose tracking

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