SiLabs xG24 Dev Kit
The Silicon Labs xG24 Dev Kit (xG24-DK2601B) is a compact, feature-packed development platform built for the EFR32MG24 Cortex-M33 microcontroller. It provides the fastest path to develop and prototype wireless IoT products. This development platform supports up to +10 dBm output power and includes support for the 20-bit ADC as well as the xG24's AI/ML hardware accelerator. The platform also features a wide variety of sensors, a microphone, Bluetooth Low Energy and a battery holder - and it's fully supported by Edge Impulse! You'll be able to sample raw data as well as build and deploy trained machine learning models directly from the Edge Impulse Studio - and even stream your machine learning results over BLE to a phone.
Silicon Labs xG24 Dev Kit Hardware Layout
To set this device up with Edge Impulse, you will need to install the following software:
Edge Impulse Studio can collect data directly from your xG24 Dev Kit and also help you trigger in-system inferences to debug your model, but in order to allow Edge Impulse Studio to interact with your xG24 Dev Kit you first need to flash it with our base firmware image.
Connecting the xG24 Dev Kit to your computer
Connecting the xG24 Dev Kit to Simplicity Commander
Then go to the "Flash" section on the left sidebar, and select the base firmware image file you downloaded in the first step above (i.e., the file named
firmware-xg24.hex). You can now press the
Flashbutton to load the base firmware image onto the xG24 Dev Kit.
Flashing the xG24 Dev Kit base image
With all the software in place, it's time to connect the xG24 Dev Kit to Edge Impulse.
Use a micro-USB cable to connect the development board to your computer.
From a command prompt or terminal, run:
This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with
Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.
Device connected to Edge Impulse.
With everything set up you can now build your first machine learning model with these tutorials: