## 2 Relevant Literature  
With the aim of ensuring a comprehensive and unbiased understanding of the current knowledge on the subject, in this section we explore the current body of literature in the field of Green AI. To do so, we use the results of a recent systematic review [11], which are provided via a replication package [[https://github.com/luiscruz/slr-green-ai](https://github.com/luiscruz/slr-green-ai)]([https://github.com/luiscruz/slr-green-ai](https://github.com/luiscruz/slr-green-ai)) (accessed on 11 September 2024) available online with an open-source license. The study goes through the analysis of 98 peer-reviewed papers, observing a notable surge in research activity since 2020. The review provides a comprehensive overview of the various topics addressed within Green AI literature. Particularly, 13 distinct areas of focus emerge, with primary attention given to monitoring, hyperparameter tuning, model benchmarking, and deployment. Less commonly explored topics, such as data-centric approaches, estimation, and emissions, reveal research gaps that warrant further investigation in the coming years. In Figure 1, authors provide the distribution of publications across different Green AI definitions, i.e., the level of abstraction used in the paper to quantify the impact of AI in the surrounding environment: energy efficiency [12], carbon footprint [13], or ecological footprint [14]. The figure shows that only 20 papers (\(\sim\)22% of primary studies) are focused on the topic of the AI carbon footprint, despite, as demonstrated in recent literature [15], reducing the environmental impact of AI exclusively to energy consumption has to be deemed an overly simplistic process. The 20 primary studies are split between observational [7; 9; 16; 20; 21; 22; 23; 24; 25; 26; 27], position [6; 22; 23; 24; 25; 26; 27], and solution [28; 29; 30; 31; 32]. The eight observational papers represent the most relevant literature for this study; hence, we briefly report their contents. [PERSON] et al. [7] present a seminal paper in which the authors assess the carbon impact of training state-of-the-art models. Figure 1: Number of publications per type of Green AI definition (source [11]). Their findings underscore the necessity of reducing the carbon footprint associated with the development and deployment of AI models, sparking significant self-reflection within the AI research community. [PERSON] et al. [9] provide the first benchmarking of Flexible Start and Pause & Resume training strategies, which will be discussed in detail in the next section. [PERSON] et al. [16] focus on understanding the carbon footprint of creating and using AI systems. Specifically, they examine the energy consumption of an AI-powered IoT scenario and report the corresponding carbon emissions based on different countries and energy sources. [PERSON] et al. [17] explore the energy impact of state-of-the-art models throughout their lifecycle. They offer an overview of the carbon footprint of AI models at Facebook, revealing that approximately 50% of the lifetime carbon cost of AI models stems from the embodied carbon of the hardware used for model development. However, they also highlight that the majority of training workflows significantly underutilize GPUs, operating at just 30-50% of their full capacity. In [18], the authors conduct a series of experiments involving twenty-seven datasets, two decision tree algorithms, and two ensemble methods for classification and regression tasks in order to assess the energy efficiency of these ML algorithms and investigate how design parameters influence energy consumption. [PERSON] et al. [19] evaluate the energy cost of various variational autoencoders, providing a valuable benchmark for comparing the energy footprint of different models and training techniques. In [20], the authors demonstrate how model distillation techniques can be used to make a machine translation system more sustainable.