# Which Design Decisions in AI-enabled Mobile Applications Contribute to Greener AI?

arXiv: https://arxiv.org/abs/2109.15284 · HTML: https://mlsanity.com/paper/2109.15284/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 58 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms](https://mlsanity.com/paper/1909.06727/) (2019)
- [Catalog of Energy Patterns for Mobile Applications](https://mlsanity.com/paper/1901.03302/) (2019)
- [Integration of Convolutional Neural Networks in Mobile Applications](https://mlsanity.com/paper/2103.07286/) (2021)
- [EMaaS: Energy Measurements as a Service for Mobile Applications](https://mlsanity.com/paper/1902.02605/) (2019)
- [Using Automatic Refactoring to Improve Energy Efficiency of Android Apps](https://mlsanity.com/paper/1803.05889/) (2018)

## Built on by (most cited first, 6 of 6)

- [Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study](https://mlsanity.com/paper/2305.11164/) (2023)
- [Energy Efficiency of Training Neural Network Architectures: An Empirical Study](https://mlsanity.com/paper/2302.00967/) (2023)
- [Towards green AI-based software systems: an architecture-centric approach (GAISSA)](https://mlsanity.com/paper/2307.09964/) (2023)
- [Innovating for Tomorrow: The Convergence of SE and Green AI](https://mlsanity.com/paper/2406.18142/) (2024)
- [Guiding the retraining of convolutional neural networks against adversarial inputs](https://mlsanity.com/paper/2207.03689/) (2022)
- [Insights into resource utilization of code small language models serving with runtime engines and execution providers](https://mlsanity.com/paper/2412.15441/) (2024)

## Abstract (arXiv)

Background: The construction, evolution and usage of complex artificial intelligence (AI) models demand expensive computational resources. While currently available high-performance computing environments support well this complexity, the deployment of AI models in mobile devices, which is an increasing trend, is challenging. Mobile applications consist of environments with low computational resources and hence imply limitations in the design decisions during the AI-enabled software engineering lifecycle that balance the trade-off between the accuracy and the complexity of the mobile applications. Objective: Our objective is to systematically assess the trade-off between accuracy and complexity when deploying complex AI models (e.g. neural networks) to mobile devices, which have an implicit resource limitation. We aim to cover (i) the impact of the design decisions on the achievement of high-accuracy and low resource-consumption implementations; and (ii) the validation of profiling tools for systematically promoting greener AI. Method: This confirmatory registered report consists of a plan to conduct an empirical study to quantify the implications of the design decisions on AI-enabled applications performance and to report experiences of the end-to-end AI-enabled software engineering lifecycle. Concretely, we will implement both image-based and language-based neural networks in mobile applications to solve multiple image classification and text classification problems on different benchmark datasets. Overall, we plan to model the accuracy and complexity of AI-enabled applications in operation with respect to their design decisions and will provide tools for allowing practitioners to gain consciousness of the quantitative relationship between the design decisions and the green characteristics of study.