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.

翻译：背景：复杂人工智能（AI）模型的构建、演进和使用需要昂贵的计算资源。尽管当前可用的高性能计算环境能够很好地支持这种复杂性，但在移动设备上部署AI模型（这一趋势日益增长）仍面临挑战。移动应用是计算资源有限的环境，因此在AI驱动软件工程生命周期的设计决策中需要权衡移动应用的准确性与复杂性之间的平衡。目标：我们的目标是系统评估在资源受限的移动设备上部署复杂AI模型（如神经网络）时准确性与复杂性之间的权衡。我们旨在涵盖：（i）设计决策对实现高准确性和低资源消耗实现的影响；（ii）验证用于系统性促进绿色AI的性能分析工具。方法：本验证性预注册报告包含一项实证研究计划，旨在量化设计决策对AI驱动应用性能的影响，并报告端到端AI驱动软件工程生命周期中的经验。具体而言，我们将在移动应用中实现基于图像和语言的神经网络，以解决多个基准数据集上的图像分类和文本分类问题。总体而言，我们计划对运行中的AI驱动应用的准确性和复杂性进行建模，并基于其设计决策提供工具，使从业者能够意识到设计决策与绿色研究特性之间的定量关系。