Running machine learning (ML) models on microcontrollers is one of the most exciting developments of the past years, allowing small battery-powered devices to detect complex motions, recognize sounds, or find anomalies in sensor data. To make it easy to build and deploy these models on ultra-low power silicon we have partnered with Silicon Labs to bring support for the Thunderboard Sense 2 to Edge Impulse - giving developers an easy way to collect data, build models, and then deploy to any EFR32/EFM32 MCU.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/tinyml-for-silabs