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Google Coral USB Accelerator
  • Google Coral USB Accelerator

Google Coral USB Accelerator

99,00 €

A USB accessory that brings machine learning inferencing to existing systems. Works with Raspberry Pi and other Linux systems. Gravitylink.

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The Coral USB Accelerator is a USB device that adds an Edge TPU coprocessor to your system. It includes a USB socket so you can connect it to any Linux-based system to perform 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. For example, it can execute state-of-the-art mobile vision models such as MobileNet v2 at 400 fps, in a power efficient manner.

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Local Inferencing
Run on-device ML inferencing on the Edge TPU designed by Google.

Works with Debian Linux
Connect to any Linux-based system with an included USB Type-C cable.

Supports TensorFlow Lite
No need to build models from the ground up. TensorFlow Lite models can be compiled to run on USB Accelerator.

Features:

· Google Edge TPU ML accelerator coprocessor
· USB 3.0 Type-C socket
· Supports Debian Linux on host CPU

System Requirements:
The Coral USB Accelerator must be connected to a host computer with the following specifications:

· Any Linux computer with a USB 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

Tech Specs

https://coral.withgoogle.com/products/accelerator/

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Tech Specs

ML accelerator Google Edge TPU coprocessor
Connector USB Type-C* (data / power)
Dimensions 65 mm x 30 mm

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* Compatible with Raspberry Pi boards at USB 2.0 speeds only.<spanyes';font-family:calibri;mso-fareast-font-family:'brown light';="" mso-hansi-font-family:'brown="" light';mso-bidi-font-family:'brown="" light';color:rgb(74,74,74);="" letter-spacing:-0.2500pt;text-transform:none;font-style:normal;="" font-size:11.0000pt;mso-font-kerning:1.0000pt;background:rgb(255,255,255);="" mso-shading:rgb(255,255,255);"="" style="-webkit-tap-highlight-color: transparent; box-sizing: border-box;">

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TensorFlow Lite

R3D-USBACC