# Hardware benchmarking application
Source: [https://docs.qualcomm.com/doc/80-70023-50/topic/vision_ai_demo_app.html](https://docs.qualcomm.com/doc/80-70023-50/topic/vision_ai_demo_app.html)
The hardware benchmarking application monitors the device hardware usage for a
defined set of sample applications to capture metrics such as CPU/GPU/NPU usage and device
thermals. These metrics explain the resource usage and throttling, which help to tune your
AI use cases according to the requirements.
Note: This application isn't supported on the Ubuntu Server.
The following figure shows the pipeline, which processes the input from a set of USB
cameras to generate various outputs.
For more information about the plugins used in these pipelines, see [Pipeline flow](https://docs.qualcomm.com/doc/80-70023-50/topic/vision_ai_demo_app.html#vision_ai_demo_app__section_znh_hmx_tfc).
## Sample model and label files
Table : Sample model and label files for hardware benchmarking application
| Runtime | Model files | Label files |
| :--- | :--- | :--- |
| LiteRT |
inception_v3_quantized.tflite
deeplabv3_plus_mobilenet_quantized
hrnet_pose_quantized.tflite
midas_quantized.tflite
yolox_quantized.tflite
|
classification.json
deeplabv3_resnet50.json
hrnet_pose.json
monodepth.json
yolox.json
|
## Set up the target device
1. To access the target device from your Linux host computer, set up SSH. For
instructions, see [Sign in using SSH](https://docs.qualcomm.com/bundle/publicresource/topics/80-70023-254/how_to.html#use-ssh).
Note: If SSH is already set up, you can skip this
step.
2. Use the HDMI port to connect the display to the device. For instructions, see
[Set up HDMI display](https://docs.qualcomm.com/bundle/publicresource/topics/80-70023-18/samples.html).
If you face
issues with display, see [Display troubleshooting](https://docs.qualcomm.com/bundle/publicresource/topics/80-70023-18/debug.html).
3. Connect two USB cameras and a mouse to the target device.
If you face any
issues with the camera or mouse connectivity, update the USB firmware. For
more information, see [FAQs](https://docs.qualcomm.com/bundle/publicresource/topics/80-70023-251/faqs.html#usb-firmware).
4. Install the Qualcomm^®^ Profiler on the Linux host computer. For
installation instructions see [Qualcomm Profiler](https://docs.qualcomm.com/bundle/publicresource/topics/80-70023-10/13-performance_tools.html#qualcomm-profiler).
After connecting
the device to the PC, run InstallerLE from the following locations:
- For
Linux:
cd /opt/qcom/Shared/QualcommProfiler/API/target-le
./InstallerLECopy to clipboard
- For
Windows:
cd “C:\Program Files (x86)\Qualcomm\Shared\QualcommProfiler\API\target-le”
.\InstallerLE.exeCopy to clipboard
## Run the application on the target device
1. Sign in to the target device over SSH and download the script to set up the
hardware benchmarking
application:
cd /opt && curl -L -O https://raw.githubusercontent.com/quic/sample-apps-for-qualcomm-linux/refs/heads/main/qualcomm-linux/scripts/setup_hw_benchmarking_app.shCopy to clipboard
2. Set permissions for the following
script:
chmod +x setup_hw_benchmarking_app.shCopy to clipboard
3. Set up the benchmarking
application:
bash setup_hw_benchmarking_app.shCopy to clipboard
4. Start the
application:
cd QCS6490-Vision-AI-Demo && bash launch_visionai_with_env.shCopy to clipboard
5. Select the preferred sample applications from the **Camera 1** and **Camera 2** drop-downs. The system thermal and hardware usage details appear at
the bottom of the screen.
You may run different sample applications to check
the output and understand the hardware utilization.
- Example 1: Choose the **Camera** option from **Camera 1** and
**Camera 2** drop-down lists to observe the preview streams
on the screen.
- Example 2: Choose any sample application from **Camera 1** and
**Camera 2** drop-down lists to observe the AI inferencing
camera streams on the screen.
Note: For more information and features of
the application, select the **Info** icon.
6. Select the **Exit** icon to close the application.
## Pipeline flow
The following table lists the plugins used in hardware benchmarking
application pipelines:
| **Plugin** | **Description** |
| --- | --- |
| Camera source: v4l2src |
After the inference runtime receives the tensor stream on its sink pad, it runs the inference.
Produces a tensor stream with the inference results on its source pad.
|
| Postprocessing plugins | The qtimlpostprocess for classification use case handles inference results from any classification model.
Applies a threshold to the chosen number of results.
Loads the MobileNet postprocessing module.
Produces results as video frames with classification labels.
Sends these processed results to the sink pad of qtivcomposer.
|
| Postprocessing plugins | The qtimlpostprocess for detection use case handles inference results from any object detection model.
Applies a threshold to the chosen number of results.
Loads the YOLO (YOLOv5, YOLOv8, YOLOX, or YOLO-NAS) module.
Produces video frames with only bounding boxes that can be overlaid on objects.
Sends these processed frames to the sink pad of qtivcomposer.
|
| Postprocessing plugins | The qtimlpostprocess for segmentation use case converts the inference tensors that it receives on its sink pad into video formats that the multimedia plugins can use for further processing. |
| Postprocessing plugins | The qtimlpostprocess for pose estimation use case
Applies a threshold to the chosen number of results.
Loads corresponding modules for various pose detection models.
In the use case described in this section, qtimlpostprocess does the following:
Loads the HRNet module.
Produces results in the form of video frames with drawn poses.
Sends the results to the sink pad of qtivcomposer for further processing or display.
|
| | |
| | |
| | |
## Known issues
- Unexpected crashes in the device are observed occasionally. Restart the
device.
- GPU usage may be shown as 0 due to Qualcomm Profiler limitations on the
platform.
- Two USB Cameras operating in YUYV space may not work simultaneously. To check if
your camera is in YUYV space, see [Prerequisite: Obtain image format and
size](https://docs.qualcomm.com/bundle/publicresource/topics/80-70023-8/usb.html#prerequisite-obtain-image-format-and-size). You may chose to use one MIPI camera and one USB camera in such
a scenario.
- The CPU and DDR thermals are fixed at 35 ℃ for Dragonwing IQ-8275 and Dragonwing
IQ-9075.
**Parent Topic:** [Run AI/ML sample applications](https://docs.qualcomm.com/doc/80-70023-50/topic/ai-ml-sample-applications.html)
Last Published: Mar 27, 2026
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