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.

DURING BETA period

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.