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

arXiv: https://arxiv.org/abs/2307.09964 · HTML: https://mlsanity.com/paper/2307.09964/

## 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 (most cited first, 14 of 14)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study](https://mlsanity.com/paper/2305.11164/) (2023)
- [Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2103.09783/) (2021)
- [Energy Efficiency of Training Neural Network Architectures: An Empirical Study](https://mlsanity.com/paper/2302.00967/) (2023)
- [Integration of Convolutional Neural Networks in Mobile Applications](https://mlsanity.com/paper/2103.07286/) (2021)
- [Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?](https://mlsanity.com/paper/2109.15284/) (2021)
- [Energy Consumption of Automated Program Repair](https://mlsanity.com/paper/2211.12104/) (2022)
- [Teaching MLOps in Higher Education through Project-Based Learning](https://mlsanity.com/paper/2302.01048/) (2023)
- [Guiding the retraining of convolutional neural networks against adversarial inputs](https://mlsanity.com/paper/2207.03689/) (2022)

## Built on by (most cited first, 3 of 3)

- [Innovating for Tomorrow: The Convergence of SE and Green AI](https://mlsanity.com/paper/2406.18142/) (2024)
- [Insights into resource utilization of code small language models serving with runtime engines and execution providers](https://mlsanity.com/paper/2412.15441/) (2024)
- [Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts](https://mlsanity.com/paper/2511.07090/) (2025)

## 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.