Academic Journal
Which design decisions in AI-enabled mobile applications contribute to greener AI?
| العنوان: | Which design decisions in AI-enabled mobile applications contribute to greener AI? |
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| المؤلفون: | Creus Castanyer, Roger, Martínez Fernández, Silverio Juan, Franch Gutiérrez, Javier |
| المساهمون: | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació, Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Services, Information and Data Engineering |
| بيانات النشر: | Springer Nature |
| سنة النشر: | 2024 |
| المجموعة: | Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
| مصطلحات موضوعية: | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial, Neural networks (Computer science), Mobile apps, Energy consumption, AI-enabled applications, Mobile applications, Model accuracy, Application performance, Greener AI, Xarxes neuronals (Informàtica), Aplicacions mòbils, Energia -- Consum |
| الوصف: | Background: The usage of complex artificial intelligence (AI) models demands expensive computational resources. While currently, available high-performance computing environments can support such complexity, the deployment of AI models in mobile devices, which is an increasing trend, is challenging. Environments with low computational resources 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 in pursuit of greener AI solutions. 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: We implement neural networks in mobile applications to solve multiple image and text classification problems on a variety of benchmark datasets. We then profile and model the accuracy, storage weight, and time of CPU usage of the AI-enabled applications in operation with respect to their design decisions. Finally, we provide an open-source data repository following the EMSE open science practices and containing all the experimentation, analysis, and reports in our study. Results: We find that the number of parameters in the AI models makes the time of CPU usage scale exponentially in convolutional neural networks and logarithmically in fully-connected layers. We also see the storage weight scales linearly with the number of parameters, while the accuracy does not. For this reason, we argue that a good practice for practitioners is to start small and only increase the size of the AI models when their accuracy is low. We also find that Residual Networks (ResNets) and Transformers have a higher baseline cost in time of CPU usage than simple convolutional and ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | 34 p.; application/pdf |
| اللغة: | English |
| Relation: | https://link.springer.com/article/10.1007/s10664-023-10407-7; info:eu-repo/grantAgreement/AEI/PLAN ESTATAL DE INVESTIGACIÓN CIENTÍFICA Y TÉCNICA Y DE INNOVACIÓN 2021-2023/TED2021-130923B-I00/GAISSA. Transición hacia sistemas de software verdes basados en IA: un enfoque centrado en arquitectura; http://hdl.handle.net/2117/400753 |
| DOI: | 10.1007/s10664-023-10407-7 |
| الاتاحة: | http://hdl.handle.net/2117/400753 https://doi.org/10.1007/s10664-023-10407-7 |
| Rights: | Open Access |
| رقم الانضمام: | edsbas.28E2728C |
| قاعدة البيانات: | BASE |
| DOI: | 10.1007/s10664-023-10407-7 |
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