You can try in below link ( It takes max 30 minutes in converting )
Introduce
- This project’s main goal is making NPU usage more easily for client
- client can know the process before using NPU for AI Service
- Whole process can be done with web UI
- Upload gpu trained model
- Test that model can be run on NPU
- Calibrate and Quantize by selected algorithm
- Download ENF converted Model
- Test directly on UI (Yolo, vision only)
Making EasyConvert v2
Key Learnings and Insights
This project focused on simplifying NPU adoption by providing an intuitive end-to-end model conversion workflow through a web-based UI.
- Developing a User-Friendly Interface for AI Model Conversion
- Learned how to design a UI/UX that makes complex AI chip usage accessible to users with minimal technical expertise.
- Focused on streamlining model upload, NPU compatibility testing, quantization, and conversion into an easy-to-follow web process.
- Bridging the Gap Between AI Users and NPU Technology
- Realized the importance of educating clients on NPU workflows before they adopt the hardware for AI services.
- Explored ways to improve visibility into the NPU conversion process, ensuring users understand each step before finalizing their models.
- Future Considerations for NPU Adoption
- Gained insights into the challenges of wider NPU adoption, particularly in terms of model compatibility, calibration methods, and developer accessibility.
- Recognized the need for better documentation, automated optimizations, and seamless integration to make NPUs more appealing to the broader AI community.
This project reinforced my understanding of how AI infrastructure should be designed for accessibility, and it provided valuable experience in developing user-friendly tools for AI hardware deployment.