Microcontrollers have no GPU, no cloud connection, and sometimes less than 256KB of RAM. TinyML is transforming edge computing by enabling smart inference directly on microcontrollers.

Zephyr RTOS, with its lightweight footprint, modular build system, and growing hardware support, has become one of the most capable platforms for building production-grade embedded AI systems.

This session is a practical, hardware-grounded walkthrough of what it actually takes to deploy and optimize TinyML workloads on Zephyr.

We will cover how to evaluate and select the right inference runtime for your hardware constraints, comparing TensorFlow Lite Micro, microTVM, emlearn, and LiteRT across memory footprint, operator support, and ease of integration.

We will walk through Zephyr's Linkable Loadable Extensions (LLEXT), which allow models to be hot-swapped at runtime without reflashing the device, a critical capability for OTA model updates in the field.

On the optimization side, we will go hands-on with quantization and operator fusion, and benchmark the results on physical hardware against Renode simulation, so you understand exactly where simulation diverges from reality.