Have you ever wondered how to make AI technology more than just a “cloud-exclusive,” and actually bring it into various devices for rapid deployment? Today, we’ll take you inside Advantech’s engineering team’s latest AI experiment, guiding you through the entire process of how Qualcomm AI Hub makes AI deployment simple and smart!
What is Qualcomm AI Hub? The Ultimate Ally for AI Developers! #
Imagine being able to deploy your trained image, voice, or speaker recognition AI models to various edge devices within just a few minutes. This is no longer a dream—Qualcomm® AI Hub was designed to make it a reality!
Qualcomm AI Hub is a one-stop AI model deployment platform that supports a wide range of AI models and devices. Whether you’re a developer, system integrator, or business owner, you can quickly find suitable models, instantly evaluate device performance, and deploy directly to your edge devices.
Want a quick overview? Check out the official introduction video.
Technical Background: Why Is Edge AI So Popular? #
With the explosive growth of IoT and smart applications, AI is no longer just “reserved for the cloud.” From smart surveillance and factory automation to intelligent retail, Edge AI Inference is transforming the rules of the industry.
- Real-Time Response: Edge AI eliminates the need to send data back to the cloud, resulting in ultra-low latency and rapid responses.
- Data Security: Sensitive information stays onsite, giving enterprises greater peace of mind.
- Flexible Deployment: Choose the optimal AI model for different devices and applications with flexibility.
The demand for “efficiently deploying AI across various hardware” is stronger than ever in the market. That’s exactly why we chose Qualcomm AI Hub for our experiment!
Live from Advantech Labs: One-Click AI Model Deployment in Action #
This time, we focused on the high-performance Qualcomm QCS6490 AI processor, validating the workflow and performance of deploying an object detection model using AI Hub. Follow along as we walk you through the experience!
Step 1. Log in to AI Hub, Select Your Device and Model #
- Visit the AI Hub official website.
- Select the target device QCS6490; the system will automatically display supported AI models and applications.
- For object detection, we chose the current mainstream YOLOv8 quantized version.
Step 2. Quickly Grasp Model Performance Data #
On the model details page, you can clearly see:
- Model name and use case (e.g., object detection)
- Actual inference time on QCS6490, memory usage
This data directly helps you assess which model best fits your application scenario.
Step-by-Step! How to Test AI Hub’s Capabilities Locally? #
System Requirements #
| Item | Specification | Notes |
|---|---|---|
| Platform | Intel 10th~13th Gen CPU | x86_64 architecture |
| Operating System | Ubuntu 22.04 | Python 3.10 required |
- The host must be x86_64 architecture and connected to the Internet
Operation Procedure #
-
Set Up the Development Environment
python3 -m venv env-ai-hub -
Install the AI Hub Package
cd env-ai-hub source bin/activate pip install qai_hub_models -
Register and Log In to Qualcomm AI Hub
- Apply for a Qualcomm® ID
- Log in to Qualcomm® AI Hub
- Set API token:
qai-hub configure --api_token API_TOKEN
-
Install YOLOv7 Object Detection Model
pip install "qai_hub_models[yolov7]"For more examples, refer to AI-Hub Models Sample
-
Query Supported Devices
qai-hub list-devices
-
Run YOLOv7 Model Test
python -m qai_hub_models.models.yolov7.demo --on-device --device "QCS6490 (Proxy)" --device-os 12 -
Observe Inference Results
- Results will be displayed directly on the host
- Results will be displayed directly on the host
Highlights of Experimental Results: AI Deployment Has Never Been Easier! #
This experiment proved that, with Qualcomm AI Hub:
- Model selection, performance evaluation, and deployment testing are seamlessly integrated—no complicated setup required, significantly lowering the development threshold.
- Supports a wide range of mainstream AI models and devices, offering great flexibility and limitless application scenarios.
- Model deployment and performance verification can be completed in just minutes, greatly shortening time-to-market.
This not only gives Advantech’s product development greater agility, but also allows our partners and clients to enjoy the innovative value of AI even faster.
Conclusion & Outlook: The Infinite Future of Edge AI Applications! #
AI is rapidly moving towards an era of “ubiquity.” This Advantech and Qualcomm AI Hub experiment has demonstrated a new level of efficiency and flexibility in AI deployment.
In the future, we will continue investing in AI R&D, exploring more industry application scenarios, and helping clients quickly adopt and create greater value. If you are interested in edge AI computing, smart applications, or want to learn more about advanced use cases, feel free to contact us to jointly shape the smart future!
Advantech is committed to continuous innovation, becoming your best partner in edge AI transformation!