Friday, March 8, 2019

The New Corel USB Accelerator module adds Edge TPU co-processor to your system for AI development. Ideel for MobileNet v2 (100+ fps) development.

The New Coral USB Accelerator module adds Edge TPU co-processor to your system for AI development. Ideal for MobileNet v2 (100+ fps) development.

Coral USB Accelerator dongle

The Coral USB Accelerator dongle is a USB device that adds an Edge TPU co-processor to your Linux development system. It includes an USB3 socket and it dose accelerated ML inferencing.

The onboard Edge TPU is a small ASIC designed by Google that provides high performance ML inferencing with a low power cost.
The unit can execute state-of-the-art mobile vision models such as MobileNet v2 at 100+ fps, in a power efficient manner.

What can I do with this Unit?

You can execute your your TensorFlow Lite models against the device.


Currently, the Edge TPU compiler requires that your model use one of the following architectures:

    MobileNet V1/V2:
    224x224 max input size; 1.0 max depth multiplier
    MobileNet SSD V1/V2:
    320x320 max input size; 1.0 max depth multiplier
    Inception V1/V2:
    224x224 fixed input size
    Inception V3/V4:
    299x299 fixed input size

All models must be a quantized TensorFlow Lite model (.tflite file) less than 100MB.
The restriction above will be removed.
The first-generation Edge TPU is capable of executing deep feed-forward neural networks (DFF) such as convolutional neural networks (CNN), making it ideal for a variety of vision-based ML applications.

Example Models available.
  • Object recognition.
  • Insect recognition.
  • Plants recognition.
  • Baird recognition.
  • Face recognition. 
  • ...

Can the Edge TPU perform accelerated ML training?

Sort of. The Edge TPU is not capable of backward propagation, which is required to perform traditional training on a model. However, using a technique described in Low-Shot Learning with Imprinted Weights, you can perform accelerated transfer-learning on the Edge TPU by embedding new vectors into the weights of the last fully-connected layer on a specially-built and pre-trained convolutional neural network (CNN).
USB Accelerator dongle
USB Accelerator dongle

What would you need to use to USB Accelerator?

Any Linux computer with a USB port (preferably USB3 port)
  • Debian 6.0 or higher, or any derivative thereof (such as Ubuntu 10.0+)
  • System architecture of either x86_64 or ARM64 with ARMv8 instruction set.

Physical size.

It has a very small footprint as can be seen in diagram below.

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