Toward Green AI: A Methodological Survey of the Scientific Literature

The pervasive deployment of Deep Learning models has recently prompted apprehensions regarding their ecological footprint, owing to the exorbitant levels of energy consumption necessitated by the training and inference processes. The term "Red AI" is employed to denote artificial intelligence (AI) models that undergo training using resource-intensive methodologies on very large datasets. This practice can engender substantial energy usage and emissions of carbon, thereby opposing "Green AI. " The latter concept alludes to AI models designed for similar efficiency and reduced environmental impact. This objective is realized through the utilization of smaller datasets, less computationally intensive training techniques, or sustainable energy resources. While Red AI prioritizes accuracy and performance, Green AI emphasizes efficiency and sustainability. Given that both paradigms exhibit advantages and limitations, the debates around the topics have burgeoned in the scientific arena, delving into novel algorithms, hardware innovations, and improved data utilization techniques aimed at mitigating the ecological consequences of intricate applications such as GPT and BERT. Nevertheless, due to the relative novelty of this debate, not much effort has been dedicated yet to contextualizing the essence of Red AI and the prospects of Green AI in a coherent framework. Within this context, the present work contributes by meticulously delineating both domains through a multifaceted analysis of their causes and ramifications, described from the points of computer architectures, data structures, and algorithms. Additionally, the study reviews notable instances of study cases based on complex Red AI models. The primary contribution of this article encompasses a comprehensive survey of Red and Green AI, stemming from a selection of the literature performed by the authors, subsequently organized into distinct clusters. These clusters encompass i) articles that qualitatively or quantitatively address the issue of Red AI, identifying Green AI as a plausible remedy, ii) articles offering insights into the environmental impact associated with the deployment of extensive Deep Learning models, and iii) articles introducing the techniques underpinning Green AI, aiming at mitigating the cost of Red AI. The outcome emerging from the analysis performed by this work consists of a compromise between sustainability in contrast to the performance of AI tools. Unless the complex training and inference procedures of software models mitigate their environmental impact, it will be necessary to decrease the level of accuracy of production systems, inevitably conflicting with the objective of the major AI vendors. The outcomes of this work would be beneficial to scholars pursuing intricate Deep Learning architectures in scientific research, as well as AI enterprises struggling with the protracted training demands of commercial products within the realms of Computer Vision and Natural Language Processing.