Google Coral TPU USB-Accelarator CPU card

The Google Coral USB Accelerator brings real-time inference to your Pi 4 and many other computers!
Artificial intelligence / machine learning for all: Google has connected a powerful special chip (TPU, Tensor Processing Unit) with the Coral USB Accelerator to a USB 3 interface - with this, Tensor Flow Lite models can be used quickly and energy-saving for inference. A particular advantage of this solution is that your data remains local. This helps with latency, and of course with data protection!
Google is increasingly using artificial intelligence (AI) and machine learning (ML) to realize its services. To do this, it developed specialized processors called TPU ("tensor processing Unit") for its data centers, which can execute the algorithms faster and more energy-saving with the TensorFlow Framework. For example, Google Maps is enhanced by street-view street signs that are analyzed using a TensorFlow-based neural network. The clou: TensorFlow can be easily programd in Python.
Google is launching a USB 3 stick with the Edge TPU, which supports the TensorFlow Lite Framework. The Edge TPU can perform up to 4 trillion arithmetic operations per second with only 2 W consumption.

Perfect in combination with the Pi 4!

With Google Coral Edge TPU, Inference can be run up to 20 times faster with the MobileNet v2 model than on "the naked" Pi 4. Real-time discoveries can be made in video streams with more than 50 fps, which would not be possible with the Pi 4 without an accelerator.
Thanks to Python and many examples online around TensorFlow, you can start the topic artificial intelligence and machine learning with the Google Coral USB Accelerator easily and with style.

Specifications Coral USB Accelerator
• Google Edge TPU ML accelerator coprocessor
• USB 3.0 (USB 3.1 Gen 1) Type C socket
• supports Linux, Mac and Windows on the host system
• Power consumption up to 900 mA Peak @ 5 V
• Dimensions Coral USB Stick: 65 mm x 30 mm x 8 mm

Host system requirements
• Linux Debian 6.0 or higher, or a derivative thereof (e.g. Ubuntu 10.0+, Raspbian)
• System architecture: x86-64, ARMv7 (32-bit) or ARMv8 (64-bit)
• MacOS 10.15 with either MacPorts or Homebrew installed
• Windows 10
• a free USB port (should be USB 3 for best performance)
• Python 3.5, 3.6 or 3.7

Scope of Supply Google Coral USB Accelerator
• USB Accelerator
• USB 3 Cable

Google provides several interesting examples and tutorials in the Coral.ai project, for example a "variant" of AlphaGo Zero is called the MiniGo.
Potential for industrial applications
of Google Coral USB Accelerator is a revolutionary product, similar to the Raspberry Pi, for machine learning applications! This enables embedded solutions that can detect problems with workpieces, detect traffic conditions, and much more.
Downloads & Documentation
• USB Accelerator Data Sheet (PDF data sheet)
• 3D CAD file in STEP format
• Edge TPU inferencing overview (Tensor Flow Lite models)
• TensorFlow models on the Edge TPU
• Pipeline C++ API Reference
• Edge TPU Python API

Important Note: The housing also serves the passive cooling of the CPU and thus we warm. This is not a defect.

This text is machine translated.