  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.
