.png?width=1120&upscale=true&name=V3_DeepLearning_Qualcomm_C1_Banner_2070x1080%20(1).png)
Introduction to On-Device AI, a new short course made in collaboration with Qualcomm and taught by Krishna Sridhar, Senior Director of Engineering at Qualcomm, is live!
As AI moves beyond the cloud, on-device inference is rapidly expanding to smartphones, IoT devices, robots, AR/VR headsets, and more. Billions of mobile and other edge devices are ready to run optimized AI models.
.gif?width=1120&upscale=true&name=Launch%20email%20GIFs%20(19).gif)
In this course, you’ll learn how to deploy AI models on edge devices using their local compute power for faster and more secure inference:
Explore how deploying models on device reduces latency, enhances efficiency, and preserves privacy.
Go through key concepts of on-device deployment such as neural network graph capture, on-device compilation, and hardware acceleration.
Convert pretrained models from PyTorch and TensorFlow for on-device compatibility.
Deploy a real-time image segmentation model on device with just a few lines of code.
Test your model performance and validate numerical accuracy when deploying to on-device environments
Quantize and make your model up to 4x faster and 4x smaller for higher on-device performance.
See a demonstration of the steps for integrating the model into a functioning Android app.
Start deploying AI models from the cloud to smartphones and edge devices!
- Learn to deploy AI models on edge devices like smartphones, using their local compute power for faster and more secure inference.
- Explore model conversion by, converting your PyTorch/TensorFlow models for device compatibility, and quantize them to achieve performance gains while reducing model size.
- Learn about device integration, including runtime dependencies, and how GPU, NPU, and CPU compute unit utilization affect performance.
https://learn.deeplearning.ai/courses/introduction-to-on-device-ai
| Lesson | Video | Code |
|---|---|---|
| Introduction | video | |
| Why on-device | video | |
| Deploying Segmentation Models On-Device | video | code |
| Preparing for on-device deployment | video | code |
| Quantizing Models | video | code |
| Device Integration | video | |
| Conclusion | video | |
| Appendix - Building the App | code | |
| Appendix - Tips and Help | code |