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.

翻译：人工智能与机器学习的快速普及引发了对其环境影响及设计环境友好型机器学习系统相关挑战的日益关注。尽管绿色人工智能研究（即致力于最小化人工智能能耗的研究）正获得越来越多的关注，但关于如何设计更具环境可持续性的机器学习系统，目前仍缺乏具体的指导原则。本文通过提出包含30项绿色架构策略的目录来填补这一空白。架构策略是一种提升软件质量的高层设计技术，在本研究中特指环境可持续性。这些策略源自对51篇主要探讨绿色人工智能的同行评审文献的分析，并采用焦点小组方法邀请三位专家进行了验证。我们提出的30项策略旨在作为从软件工程视角进一步探索绿色人工智能的初步参考指南，并协助设计可持续的机器学习系统。为提升透明度并促进其广泛使用与扩展，我们将以易于使用的格式在线公开这些策略。这些策略的广泛采用有望显著降低机器学习系统在能源消耗与碳足迹方面的社会影响。