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

by   Roger Creus Castanyer, et al.

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.


page 1

page 2

page 3

page 4


EPAM: A Predictive Energy Model for Mobile AI

Artificial intelligence (AI) has enabled a new paradigm of smart applica...

Towards Operationalising Responsible AI: An Empirical Study

While artificial intelligence (AI) has great potential to transform many...

Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications

Deep neural networks (DNNs) have achieved unprecedented success in the f...

AI Benchmark: Running Deep Neural Networks on Android Smartphones

Over the last years, the computational power of mobile devices such as s...

Priority Quality Attributes for Engineering AI-enabled Systems

Deploying successful software-reliant systems that address their mission...

LeanAI: A method for AEC practitioners to effectively plan AI implementations

Recent developments in Artificial Intelligence (AI) provide unprecedente...

Structured Bayesian Compression for Deep models in mobile enabled devices for connected healthcare

Deep Models, typically Deep neural networks, have millions of parameters...

Please sign up or login with your details

Forgot password? Click here to reset