Knowledge requiredThis tutorial assumes that you’re familiar with building applications for Sony’s Spresense. If you’re unfamiliar with either of these you build binaries directly for your development board from the Deployment page in the studio.
Prerequisites
Make sure you’ve followed one of the tutorials and have a trained impulse. Also install the following software:- Edge Impulse CLI.
- GNU Make.
- GNU ARM Embedded Toolchain 8-2018-q4-major - make sure
arm-none-eabi-gcc
is in your PATH.
Cloning the base repository
We created an example repository which contains a small application for Sony’s Spresense, which takes the raw features as an argument, and prints out the final classification. Download the application here, or import this repository using Git:Deploying your impulse
Head over to your Edge Impulse project, and go to the Deployment tab. From here you can create the full library which contains the impulse and all required libraries. Select C++ library and click Build to create the library. Download the.zip
file and extract the directories in the example-standalone-inferencing-spresense/edge_impulse/
folder. Your final folder structure should look like this:
Running the impulse
With the project ready it’s time to verify that the application works. Head back to the studio and click on Live classification. Then load a validation sample, and click on a row under ‘Detailed result’.
Selecting the row with timestamp '320' under 'Detailed result'.

Copying the raw features.
ei_main.cpp
and paste the raw features inside the static const float features[]
definition, for example:
Building the application (make
)
-
Build the application by calling make in the root directory of the project:
- Connect the board to your computer using USB.
-
Flash the board:
Building the application (Docker)
-
Build the Docker image:
-
Build the application by running the container as follows:
Windows
Linux, macOS
- Connect the board to your computer using USB.
-
Flash the board:
Or if you don’t have
make
installed: