mlsanityData to 14 Oct 2026

Towards green AI-based software systems: an architecture-centric approach (GAISSA)

arXiv 2307.09964 ↗First posted 19 Jul 2023cs.SE cs.LGcited by 3

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Abstract (arXiv)

Nowadays, AI-based systems have achieved outstanding results and have outperformed humans in different domains. However, the processes of training AI models and inferring from them require high computational resources, which pose a significant challenge in the current energy efficiency societal demand. To cope with this challenge, this research project paper describes the main vision, goals, and expected outcomes of the GAISSA project. The GAISSA project aims at providing data scientists and software engineers tool-supported, architecture-centric methods for the modelling and development of green AI-based systems. Although the project is in an initial stage, we describe the current research results, which illustrate the potential to achieve GAISSA objectives.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 49 references that are in the map. First posted on arXiv 19 Jul 2023.

Builds on

14 of 49 references.

References · 14

  1. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  2. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
  3. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  4. 2019Green AIcited by 627
  5. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  6. 2021Software Engineering for AI-Based Systems: A Surveycited by 69
  7. 2023Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Studycited by 23
  8. 2021Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Studycited by 21
  9. 2023Energy Efficiency of Training Neural Network Architectures: An Empirical Studycited by 15
  10. 2021Integration of Convolutional Neural Networks in Mobile Applicationscited by 6
  11. 2021Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?cited by 6
  12. 2022Energy Consumption of Automated Program Repaircited by 3
2 more
  1. 2023Teaching MLOps in Higher Education through Project-Based Learningcited by 3
  2. 2022Guiding the retraining of convolutional neural networks against adversarial inputscited by 1

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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Innovating for Tomorrow: The Convergence of SE and Green AIcited by 3
  2. 2024Insights into resource utilization of code small language models serving with runtime engines and execution providerscited by 0
  3. 2025Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attemptscited by 0