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Background: The construction, evolution and usage of complex artificialintelligence (AI) models demand expensive computational resources. Whilecurrently available high-performance computing environments support well thiscomplexity, the deployment of AI models in mobile devices, which is anincreasing trend, is challenging. Mobile applications consist of environmentswith low computational resources and hence imply limitations in the designdecisions during the AI-enabled software engineering lifecycle that balance thetrade-off between the accuracy and the complexity of the mobile applications.  Objective: Our objective is to systematically assess the trade-off betweenaccuracy 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 andlow resource-consumption implementations; and (ii) the validation of profilingtools for systematically promoting greener AI.  Method: This confirmatory registered report consists of a plan to conduct anempirical study to quantify the implications of the design decisions onAI-enabled applications performance and to report experiences of the end-to-endAI-enabled software engineering lifecycle. Concretely, we will implement bothimage-based and language-based neural networks in mobile applications to solvemultiple image classification and text classification problems on differentbenchmark datasets. Overall, we plan to model the accuracy and complexity ofAI-enabled applications in operation with respect to their design decisions andwill provide tools for allowing practitioners to gain consciousness of thequantitative relationship between the design decisions and the greencharacteristics of study.

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