Coral dev board

1. Google Edge TPU
Rouyun Pan

2. Google Edge TPU
https://coral.withgoogle.com/products/

3. Dev Board datasheet
4 TOPS, 1.5W

4. System Of Module (SOM)

5. USB Accelerator
https://www.seeedstudio.com/Coral-USB-Accelerator-p-2899.html

6. Neural network accelerator
https://patents.google.com/patent/US20190050717A1/

7. Tensorflow lite

8. The basic workflow to create
a model for the Edge TPU

9. The compiler creates a single custom
op for all Edge TPU compatible ops

10. Inference Engines

11. Run multiple models with
multiple Edge TPUs

12. Imprinting Engine
• Benefits:
• Transfer-learning happens on-device, at
near-realtime speed.
• You don't need to recompile the model.
• Drawbacks:
• Training data size is limited to a max of
200 images per class.
• It is most suitable only for datasets that
have a small inner class variation
• The last fully-connected layer runs on
the CPU, not the Edge TPU. So it will
be slightly less efficient than running a
pre-compiled on Edge TPU.
https://coral.withgoogle.com/docs/edgetpu/retrain-classification-ondevice/

13. Performance
c
c

14. Perfermance

15. Comparing with Others
https://medium.com/@aallan/benchmarking-edge-computing-ce3f13942245

16. Demo
https://www.youtube.com/watch?v=AokyF2HWnqk