  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.
