  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.
