2024/03/18 by Enzo Scaffi, Scaffi, Enzo, Antoine Bonneau +5
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Cloud Computing and Resource Management #FOS: Computer and information sciences #Green IT and Sustainability #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2404.07948
openalex publication_date 2024/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This research empirically examines embedded development tools viable for on-device TinyML implementation. The research evaluates various development tools with various abstraction levels on resource-constrained IoT devices, from basic hardware manipulation to deployment of minimalistic ML training. The analysis encompasses memory usage, energy consumption, and performance metrics during model training and inference and usability of the different solutions. Arduino Framework offers ease of implementation but with increased energy consumption compared to the native option, while RIOT OS exhibits efficient energy consumption despite higher memory utilization with equivalent ease of use. The absence of certain critical functionalities like DVFS directly integrated into the OS highlights limitations for fine hardware control.