Set up a Qualcomm Dragonwing QC6490 with Ubuntu

Prepare a Qualcomm Dragonwing QC6490 with Ubuntu

The Qualcomm Dragonwing QC6490 is an Arm-based platform that supports both the on-board Qualcomm camera module and USB-attached cameras for live inference with Edge Impulse. You’ll complete prerequisites, dependency installation, and the component configuration for running the Edge Impulse Linux Runner on a QC6490 device with AWS IoT Greengrass.

Prerequisites

Before you begin, make sure you have:

• A Qualcomm Dragonwing QC6490 development board with a power supply
• Ubuntu flashed onto the device according to the Qualcomm QC6490 quick start guide
• A Network connection (Ethernet or Wi-Fi) and SSH access to the device
• (Optional) An on-board Qualcomm camera module or USB camera for live inference; without a camera, the Edge Impulse Linux Runner uses a sample video file

Connect over SSH

Connect to the QC6490 from your computer. Replace the placeholder with the device’s IP address:

    

        
        
ssh your-username@<your-qc6490-ip-address>

    

If you’re not sure of the IP address, check your router’s admin page for connected devices, or run hostname -I on the QC6490 if you have a monitor connected.

Verify Ubuntu is running

Confirm the device is running Ubuntu on aarch64:

    

        
        
uname -a

    

The output shows aarch64 as the architecture and an Ubuntu kernel version.

Install dependencies

Update the package list and install the build tools, Node.js, and GStreamer plugins that the Edge Impulse Linux Runner requires:

    

        
        
sudo apt update
sudo apt install -y curl unzip
sudo apt install -y gcc g++ make build-essential nodejs sox gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-base gstreamer1.0-plugins-base-apps

    

Greengrass Nucleus Classic is Java-based, so you also need to install a JDK:

    

        
        
sudo apt install -y default-jdk

    

Install any available security updates:

    

        
        
sudo apt upgrade -y

    

(Optional) Verify the camera

The QC6490 supports two types of cameras. The type of camera you have determines which JSON configuration you need to use:

If you’re using the on-board Qualcomm camera, it uses the qtiqmmfsrc GStreamer element, which is specific to Qualcomm platforms. If your board has a built-in camera module, it should be available without additional setup.

If you’re using a USB camera instead, confirm that the system detects it:

    

        
        
ls /dev/video*

    

The output is similar to:

    

        
        /dev/video0

        
    

If nothing appears, check that the camera is plugged in securely and try a different USB port.

Save the component configuration

The following JSON configurations set up the Edge Impulse Greengrass component for the QC6490. This platform has three configuration options depending on your camera setup. Choose the one that matches your hardware and save it to a text file on your local machine. You’ll paste it into the Greengrass deployment configuration in a later step.

With the on-board Qualcomm camera

This configuration uses the qtiqmmfsrc GStreamer element to capture video from the on-board camera at 1280 × 720 resolution. The --force-variant float32 flag selects the float32 model variant. --silent suppresses console output because the Edge Impulse Linux Runner runs as a background service:

    

        
        
{
   "Parameters": {
      "node_version": "20.18.2",
      "vips_version": "8.12.1",
      "device_name": "MyQC6490UbuntuEdgeDevice",
      "launch": "runner",
      "sleep_time_sec": 10,
      "lock_filename": "/tmp/ei_lockfile_runner",
      "gst_args": "qtiqmmfsrc:name=camsrc:camera=0:!:video/x-raw,width=1280,height=720:!:videoconvert:!:jpegenc",
      "eiparams": "--greengrass",
      "iotcore_backoff": "-1",
      "iotcore_qos": "1",
      "ei_bindir": "/usr/local/bin",
      "ei_sm_secret_id": "EI_API_KEY",
      "ei_sm_secret_name": "ei_api_key",
      "ei_poll_sleeptime_ms": 2500,
      "ei_local_model_file": "/home/ggc_user/data/currentModel.eim",
      "ei_shutdown_behavior": "__none__",
      "ei_ggc_user_groups": "video audio input users",
      "install_kvssink": "no",
      "publish_inference_base64_image": "no",
      "enable_cache_to_file": "no",
      "cache_file_directory": "__none__",
      "enable_threshold_limit": "no",
      "metrics_sleeptime_ms": 30000,
      "default_threshold": 65.0,
      "threshold_criteria": "ge",
      "enable_cache_to_s3": "no",
      "s3_bucket": "__none__"
   }
}

    

With a USB-attached camera

This configuration uses the standard v4l2src GStreamer element to capture video from a USB camera at 640 × 480 resolution. Use this configuration if your QC6490 board doesn’t have a built-in camera module, or if you prefer to use an external USB camera:

    

        
        
{
   "Parameters": {
      "node_version": "20.18.2",
      "vips_version": "8.12.1",
      "device_name": "MyQC6490UbuntuEdgeDevice",
      "launch": "runner",
      "sleep_time_sec": 10,
      "lock_filename": "/tmp/ei_lockfile_runner",
      "gst_args": "v4l2src:device=/dev/video0:!:video/x-raw,width=640,height=480:!:videoconvert:!:jpegenc",
      "eiparams": "--greengrass",
      "iotcore_backoff": "-1",
      "iotcore_qos": "1",
      "ei_bindir": "/usr/local/bin",
      "ei_sm_secret_id": "EI_API_KEY",
      "ei_sm_secret_name": "ei_api_key",
      "ei_poll_sleeptime_ms": 2500,
      "ei_local_model_file": "/home/ggc_user/data/currentModel.eim",
      "ei_shutdown_behavior": "__none__",
      "ei_ggc_user_groups": "video audio input users",
      "install_kvssink": "no",
      "publish_inference_base64_image": "no",
      "enable_cache_to_file": "no",
      "cache_file_directory": "__none__",
      "enable_threshold_limit": "no",
      "metrics_sleeptime_ms": 30000,
      "default_threshold": 65.0,
      "threshold_criteria": "ge",
      "enable_cache_to_s3": "no",
      "s3_bucket": "__none__"
   }
}

    

Without a camera

This configuration reads inference input from a local sample video file. The ei_local_model_file field points to a pre-downloaded model. ei_shutdown_behavior is set to wait_on_restart so that the Edge Impulse Linux Runner pauses after the video ends and waits for a restart command:

    

        
        
{
   "Parameters": {
      "node_version": "20.18.2",
      "vips_version": "8.12.1",
      "device_name": "MyQC6490UbuntuEdgeDevice",
      "launch": "runner",
      "sleep_time_sec": 10,
      "lock_filename": "/tmp/ei_lockfile_runner",
      "gst_args": "filesrc:location=/home/ggc_user/data/testSample.mp4:!:decodebin:!:videoconvert:!:videorate:!:video/x-raw,framerate=2200/1:!:jpegenc",
      "eiparams": "--greengrass",
      "iotcore_backoff": "-1",
      "iotcore_qos": "1",
      "ei_bindir": "/usr/local/bin",
      "ei_sm_secret_id": "EI_API_KEY",
      "ei_sm_secret_name": "ei_api_key",
      "ei_poll_sleeptime_ms": 2500,
      "ei_local_model_file": "/home/ggc_user/data/currentModel.eim",
      "ei_shutdown_behavior": "wait_on_restart",
      "ei_ggc_user_groups": "video audio input users system",
      "install_kvssink": "no",
      "publish_inference_base64_image": "no",
      "enable_cache_to_file": "no",
      "cache_file_directory": "__none__",
      "enable_threshold_limit": "no",
      "metrics_sleeptime_ms": 30000,
      "default_threshold": 50,
      "threshold_criteria": "ge",
      "enable_cache_to_s3": "no",
      "s3_bucket": "__none__"
   }
}

    

What you’ve accomplished and what’s next

You’ve prepared your Qualcomm Dragonwing QC6490, installed its dependencies, and saved the component configuration for your selected input source.

Your Qualcomm Dragonwing QC6490 is ready. Next, you’ll set up the Edge Impulse project .