The Green AI Ontology: An Ontology for Modeling the Energy Consumption of AI Models

Modeling AI systems’ characteristics of energy consumption and their sustainability level as an extension of the FAIR data principles has been considered only rudimentarily. In this paper, we propose the Green AI Ontology for modeling the energy consumption and other environmental aspects of AI models. We evaluate our ontology based on competency questions. Our ontology is available at https://w3id.org/ Green-AI-Ontology and can be used in a variety of scenarios, ranging from comprehensive research data management to strategic controlling of institutions and environmental efforts in politics

A Synthesis of Green Architectural Tactics for ML-Enabled Systems

The rapid adoption of artificial intelligence (AI) and machine learning (ML) has generated growing interest in understanding their environmental impact and the challenges associated with designing environmentally friendly ML-enabled systems. While Green AI research, i.e., research that tries to minimize the energy footprint of AI, is receiving increasing attention, very few concrete guidelines are available on how ML-enabled systems can be designed to be more environmentally sustainable. In this paper, we provide a catalog of 30 green architectural tactics for ML-enabled systems to fill this gap. An architectural tactic is a high-level design technique to improve software quality, in our case environmental sustainability. We derived the tactics from the analysis of 51 peer-reviewed publications that primarily explore Green AI, and validated them using a focus group approach with three experts. The 30 tactics we identified are aimed to serve as an initial reference guide for further exploration into Green AI from a software engineering perspective, and assist in designing sustainable ML-enabled systems. To enhance transparency and facilitate their widespread use and extension, we make the tactics available online in easily consumable formats. Wide-spread adoption of these tactics has the potential to substantially reduce the societal impact of ML-enabled systems regarding their energy and carbon footprint.Comment: Accepted for publication at the 2024 International Conference on Software Engineering - Software Engineering in Society (ICSE-SEIS'2024

Applying Green AI methods to Digital Rock Technology workflows

Peer reviewedPublisher PD

Batching for Green AI -- An Exploratory Study on Inference

The batch size is an essential parameter to tune during the development of new neural networks. Amongst other quality indicators, it has a large degree of influence on the model's accuracy, generalisability, training times and parallelisability. This fact is generally known and commonly studied. However, during the application phase of a deep learning model, when the model is utilised by an end-user for inference, we find that there is a disregard for the potential benefits of introducing a batch size. In this study, we examine the effect of input batching on the energy consumption and response times of five fully-trained neural networks for computer vision that were considered state-of-the-art at the time of their publication. The results suggest that batching has a significant effect on both of these metrics. Furthermore, we present a timeline of the energy efficiency and accuracy of neural networks over the past decade. We find that in general, energy consumption rises at a much steeper pace than accuracy and question the necessity of this evolution. Additionally, we highlight one particular network, ShuffleNetV2(2018), that achieved a competitive performance for its time while maintaining a much lower energy consumption. Nevertheless, we highlight that the results are model dependent.Comment: 8 pages, 4 figures, 1 table. Accepted at Euromicro Conference Series on Software Engineering and Advanced Applications (SEAA) 202

Green AI: Do Deep Learning Frameworks Have Different Costs?

The use of Artificial Intelligence (ai), and more specifically of Deep Learning (dl), in modern software systems, is nowadays widespread and continues to grow. At the same time, its usage is energy demanding and contributes to the increased CO2 emissions, and has a great financial cost as well. Even though there are many studies that examine the capabilities of dl, only a few focus on its green aspects, such as energy consumption. This paper aims at raising awareness of the costs incurred when using different dl frameworks. To this end, we perform a thorough empirical study to measure and compare the energy consumption and run-time performance of six different dl models written in the two most popular dl frameworks, namely PyTorch and TensorFlow. We use a well-known benchmark of dl models, DeepLearningExamples, created by nvidia, to compare both the training and inference costs of dl. Finally, we manually investigate the functions of these frameworks that took most of the time to execute in our experiments. The results of our empirical study reveal that there is a statistically significant difference between the cost incurred by the two dl frameworks in 94% of the cases studied. While TensorFlow achieves significantly better energy and run-time performance than PyTorch, and with large effect sizes in 100% of the cases for the training phase, PyTorch instead exhibits significantly better energy and run-time performance than TensorFlow in the inference phase for 66% of the cases, always, with large effect sizes. Such a large difference in performance costs does not, however, seem to affect the accuracy of the models produced, as both frameworks achieve comparable scores under the same configurations. Our manual analysis, of the documentation and source code of the functions examined, reveals that such a difference in performance costs is under-documented, in these frameworks. This suggests that developers need to improve the documentation of their dl frameworks, the source code of the functions used in these frameworks, as well as to enhance existing dl algorithms

Eco-Friendly Low Resource Security Surveillance Framework Toward Green AI Digital Twin

Most intelligent systems focused on how to improve performance including accuracy, processing speed with a massive number of data sets and those performance-biased intelligent systems, Red AI systems, have been applied to digital twin in smart cities. On the other hand, it is highly reasonable to consider Green AI features covering environmental, economic, social costs for advanced digital twin services. In this letter, we propose eco-friendly low resource security surveillance toward Green AI-enabled digital twin service, which provides eco-friendly security by the active participation of low resource devices. And, we formally define a problem whose objective is to maximize the participation of low source or reusable devices such that reusable surveillance borders are created within security district. Also, a dense sub-district with low resource devices priority completion scheme is proposed to resolve the problem. Then, the devised method is performed by expanded simulations and the achieved result is evaluated with demonstrated discussions

Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation

Fine-tuning is the most effective way of adapting pre-trained large language models (LLMs) to downstream applications. With the fast growth of LLM-enabled AI applications and democratization of open-souced LLMs, fine-tuning has become possible for non-expert individuals, but intensively performed LLM fine-tuning worldwide could result in significantly high energy consumption and carbon footprint, which may bring large environmental impact. Mitigating such environmental impact towards Green AI directly correlates to reducing the FLOPs of fine-tuning, but existing techniques on efficient LLM fine-tuning can only achieve limited reduction of such FLOPs, due to their ignorance of the backpropagation cost in fine-tuning. To address this limitation, in this paper we present GreenTrainer, a new LLM fine-tuning technique that adaptively evaluates different tensors' backpropagation costs and contributions to the fine-tuned model accuracy, to minimize the fine-tuning cost by selecting the most appropriate set of tensors in training. Such selection in GreenTrainer is made based on a given objective of FLOPs reduction, which can flexibly adapt to the carbon footprint in energy supply and the need in Green AI. Experiment results over multiple open-sourced LLM models and abstractive summarization datasets show that, compared to fine-tuning the whole LLM model, GreenTrainer can save up to 64% FLOPs in fine-tuning without any noticeable model accuracy loss. Compared to the existing fine-tuning techniques such as LoRa, GreenTrainer can achieve up to 4% improvement on model accuracy with on-par FLOPs reduction.Comment: 14 page

Uncovering Energy-Efficient Practices in Deep Learning Training:Preliminary Steps Towards Green AI

Modern AI practices all strive towards the same goal: better results. In the context of deep learning, the term "results"often refers to the achieved accuracy on a competitive problem set. In this paper, we adopt an idea from the emerging field of Green AI to consider energy consumption as a metric of equal importance to accuracy and to reduce any irrelevant tasks or energy usage. We examine the training stage of the deep learning pipeline from a sustainability perspective, through the study of hyperparameter tuning strategies and the model complexity, two factors vastly impacting the overall pipeline's energy consumption. First, we investigate the effectiveness of grid search, random search and Bayesian optimisation during hyperparameter tuning, and we find that Bayesian optimisation significantly dominates the other strategies. Furthermore, we analyse the architecture of convolutional neural networks with the energy consumption of three prominent layer types: convolutional, linear and ReLU layers. The results show that convolutional layers are the most computationally expensive by a strong margin. Additionally, we observe diminishing returns in accuracy for more energy-hungry models. The overall energy consumption of training can be halved by reducing the network complexity. In conclusion, we highlight innovative and promising energy-efficient practices for training deep learning models. To expand the application of Green AI, we advocate for a shift in the design of deep learning models, by considering the trade-off between energy efficiency and accuracy.</p