تقرير
A Synthesis of Green Architectural Tactics for ML-Enabled Systems
| العنوان: | A Synthesis of Green Architectural Tactics for ML-Enabled Systems |
|---|---|
| المؤلفون: | Järvenpää, Heli, Lago, Patricia, Bogner, Justus, Lewis, Grace, Muccini, Henry, Ozkaya, Ipek |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Software Engineering, Computer Science - Machine Learning |
| الوصف: | 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. Comment: Accepted for publication at the 2024 International Conference on Software Engineering - Software Engineering in Society (ICSE-SEIS'2024) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2312.09610 |
| رقم الأكسشن: | edsarx.2312.09610 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |