The document discusses the Google Edge TPU, which is a neural network accelerator that provides 4 TOPS of processing power while consuming only 1.5W of power. It can run models using TensorFlow Lite and allows running multiple models across multiple Edge TPUs. The document also discusses an imprinting engine that allows transfer learning to occur directly on the Edge TPU for tasks like image classification with some limitations on training data size and efficiency. Performance comparisons show it outperforming other edge devices and there are demos of it running models for tasks like object detection.

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
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14. Perfermance

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

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