Impulses can be deployed as a C++ library. This packages all your signal processing blocks, configuration and learning blocks up into a single package. You can include this package in your own application to run the impulse locally. In this tutorial you’ll export an impulse, and build an application in Simplicity Studio to classify sensor data on the SiLabs Thunderboard Sense 2 development board.
Knowledge requiredThis tutorial assumes that you’re familiar with compiling applications with Simplicity Studio. If you’re unfamiliar with this you can build binaries directly for your development board from the Deployment page in the studio.
Note: Are you looking for an example that has all sensors included? The Edge Impulse firmware for the SiLabs Thunderboard Sense 2 has that. See edgeimpulse/firmware-silabs-thunderboard-sense-2.

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Prerequisites

Make sure you followed the [Continuous motion recognition] (tutorials/continuous-motion-recognition.md) tutorial, and have a trained impulse. Also install the following software: Alternatively you can build this application from the command line or through Docker, see the build instructions in example-standalone-inferencing-silabs-tb-sense-2.

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Cloning the base repository

We created an example repository which contains a small Simplicity Studio application, which takes the raw features as an argument, and prints out the final classification. You can either download the application or import this repository using Git: 
git clone https://github.com/edgeimpulse/example-standalone-inferencing-silabs-tb-sense-2.git

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Deploying your impulse

Head over to your Edge Impulse project, and go to Deployment. From here you can create the full library which contains the impulse and all external required libraries. Select C++ library and click Build to create the library. Download the .zip file and place the contents in the ‘example-standalone-inferencing-silabs-tb-sense-2/ei-workspace/edgeimpulse’ folder (which you downloaded above).

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Importing the workspace into Simplicity Studio

With the model downloaded you can import the project into Simplicity Studio.
  1. Generate the Simplicity Studio project by opening a command prompt or terminal, navigating to the 'example-standalone-inferencing-silabs-tb-sense-2 folder and running:
$ pip3 install pyyaml
$ python3 update-slcp.py
  1. Open Simplicity IDE and install the Gecko SDK 3.2.x.
  2. Create a new project via File > New > Silicon Labs Project Wizard…
  3. In the New Project Wizard select Simplicity Studio > Silicon Labs MCU Project and click Next
  4. Under ‘board’ select Thunderboard Sense 2.
  5. Select the correct SDK you installed in #1 and click Next.
  1. Select Empty C++ Program and click Next.
  1. Name the project example-standalone-inferencing-silabs-tb-sense-2 (exactly this) and make sure Copy contents is selected before clicking Finish.
  1. Under ‘Project Explorer’ select all files, except for Includes and delete them:
  1. Then, navigate to the example-standalone-inferencing-silabs-tb-sense-2/ei-workspace folder (in this repository), and drag all files and folders into the ‘Project explorer’ window in Simplicity Studio. When prompted select Copy files and folders for this operation.
  2. Then close, and reopen the project via: Project > Close Project, then Project > Open Project.
  3. Double-click on example-standalone-inferencing-silabs-tb-sense-2.slcp to show the Simplicity Configurator.
  4. Edit ‘Project Generators’ and disable ‘IAR EMBEDDED WORKBENCH PROJECT’ (if it’s listed):
  1. Click Force Generation to regenerate all links and include paths.
  1. The project is now imported!

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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'.

To verify that the local application classifies the same, we need the raw features for this timestamp. To do so click on the ‘Copy to clipboard’ button next to ‘Raw features’. This will copy the raw values from this validation file, before any signal processing or inferencing happened.

Copying the raw features.

In the example directory open main.cpp and paste the raw features inside the static const float features[] definition, for example: 
static const float features[] = {
    -19.8800, -0.6900, 8.2300, -17.6600, -1.1300, 5.9700, ...
};
With everything set up, you can now build the application using Simplicity Studio, the command line, or with Docker.

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Building and flashing the application

  1. In Simplicity Studio v5, select Project > Build Project to build the firmware.
  2. Then, right click on the development board in the Debug adapters section of Simplicity Studio and select Upload application.
  1. Under Application image path select the GNU ARM v10.2.1 - Default/example-standalone-inferencing-silabs-tb-sense-2.bin file and click OK to flash.
(Alternatively you can drag and drop the GNU ARM v10.2.1 - Default/example-standalone-inferencing-silabs-tb-sense-2.bin file onto the TB004 mass-storage device to flash the binary.

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Seeing the output

To see the output of the impulse, connect to the development board over a serial port on baud rate 115,200 and reset the board. You can do this with your favourite serial monitor or with the Edge Impulse CLI: 
$ edge-impulse-run-impulse --raw
This will run the signal processing pipeline, and then classify the output: 
Edge Impulse standalone inferencing (Silicon Labs Thunderboard Sense 2)
Running neural network...
Predictions (time: 1 ms.):
idle:   0.015319
snake:  0.000444
updown: 0.006182
wave:   0.978056
Anomaly score (time: 0 ms.): 0.133557
Predictions (DSP: 21 ms., Classification: 1 ms., Anomaly: 0 ms.):
[0.01532, 0.00044, 0.00618, 0.97806, 0.134]
Which matches the values we just saw in the studio. You now have your impulse running on your Thunderboard Sense 2 development board!