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：人工智能和机器学习的快速普及引发了人们对其环境影响以及设计环保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源足迹的研究）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，目前可用的具体指导原则仍十分有限。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（本文特指环境可持续性）的高层设计技术。我们通过对51篇主要探讨绿色人工智能的同行评审文献进行分析，并采用焦点小组方法邀请三位专家进行验证，最终提炼出这些策略。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，我们以易于获取的在线形式公开这些策略。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和碳足迹方面的社会影响。