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（AI）和机器学习（ML）的快速普及引发了人们对其环境影响的日益关注，以及设计环境友好型机器学习系统所面临的挑战。尽管绿色AI研究（即致力于最小化AI能源足迹的研究）正受到越来越多的重视，但关于如何设计更环境可持续的机器学习系统的具体指导方针仍然非常有限。为填补这一空白，本文提供了面向机器学习系统的30种绿色架构策略的目录。架构策略是一种提升软件质量（本文中特指环境可持续性）的高层次设计技术。这些策略源自对51篇主要探讨绿色AI的同行评审文献的分析，并通过三位专家参与的焦点小组方法进行了验证。我们识别的30种策略旨在作为从软件工程视角进一步探索绿色AI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和扩展，我们将这些策略以易于消费的格式在线公开。这些策略的广泛应用有望在能源和碳足迹方面显著降低机器学习系统的社会影响。