As an Edge AI Engineer constantly exploring state-of-the-art edge AI solutions, Qualcomm is the only1 company offering a such robust and comprehensive ecosystem that includes pretrained models, frameworks, cloud services, and on-device AI platforms. This layered ecosystem provides developers with a seamless experience: even without direct access to Qualcomm hardware, models can be run and profiled in the cloud, accelerating prototyping and deployment.
I took this course to gain a deeper and broader understanding of Qualcomm ecosystem. Other Qualcomm Edge AI course includes Introduction On-Device AI from DeepLearningAI, and more here.
Screenshots or excerpts from course slides included in this repository are used solely for educational purposes, personal learning, and review. For any copyright infringement please review the DISCLAIMER.md.
- Introduction to Qualcomm AI
- Use Cases Qualcomm AI
- Connected Intelligent Edge
- Qualcomm AI Stack Introduction
- AI Model Efficiency Toolkit (AIMET)
| Chapters | Description | Notebooks |
|---|---|---|
| 1. Introduction to Qualcomm AI | Qualcomm’s Toolkit, AI Engine Direct, and AI Stack. | - |
| 2. Use Cases Qualcomm AI | Use cases for AI in mobile, automotive, compute, IoT, XR, and voice/ music. | - |
| 3. Connected Intelligent Edge | The Connected Intelligent Edge and what powers it. | - |
| 4. Qualcomm AI Stack Introduction | Qualcomm AI Stack, including Qualcomm AI Studio with a graphic user interface, visualization tools, and domain-specific SDK | - |
| 5. AIMET | AI Model Efficiency Toolkit (AIMET) that has pretrained models for image classification, semantic segmentation, super resolution, object detection, pose estimation, and speech recognition | - |
Footnotes
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As of June 2025, this may change in the future ↩