G950-01456-01/ G950-06809-01Coral USB Edge TPU ML Accelerator coprocessor for Raspberry Pi and Other Embedded Single Board Computers($129.55Value)

Description

Important Note: The AIY Maker Kit includes a USB Accelerator, a Raspberry Pi 4 (8 GB), and other useful accessories.

We also offer the Dev Board Mini and the Dev Board 4GB. For particularly high performance, we recommend using the Dev Board 4GB, while the Dev Board Mini is especially suitable for low-cost solutions.

The USB Accelerator brings real-time inference (Deep Learning / Machine Learning) to your Raspberry Pi 4 and many other computers!

AI for Everyone: A powerful specialized chip (TPU, Tensor Processing Unit) is connected to a USB 3 interface with the USB Accelerator, enabling fast and energy-efficient inference for TensorFlow Lite models.

A key advantage of this solution: your data stays local, which helps with latency and, of course, data protection! (In compliance with relevant laws, such as the General Data Protection Regulation (GDPR).)

The company behind this technology increasingly uses artificial intelligence (AI) and machine learning (ML) to realize its services. For this, it developed specialized processors called TPUs ("Tensor Processing Units") for its data centers that can execute algorithms faster and more efficiently with the TensorFlow framework. For example, a service similar to Maps improves by analyzing street signs captured by a service similar to Street View using a TensorFlow-based neural network. The bonus: TensorFlow can easily be programmed in Python.

AI for Home Use? Yes! This company is an interesting entity—they don't keep this technology to themselves but share it with the world. Early last year, a USB 3 stick with a high-performance TPU was launched, which supports the TensorFlow Lite framework. The TPU can perform up to 4 trillion operations per second with only 2 watts of power consumption. TensorFlow Lite is a modified version of TensorFlow specifically tailored to meet the needs of mobile devices and embedded systems. Many TensorFlow applications can also be realized in TensorFlow Lite.

Perfect in Combination with the Raspberry Pi 4! With the high-performance TPU, inference with the MobileNet v2 model can be performed up to 20 times faster than on "bare" Pi 4. Real-time recognition in video streams with over 50 fps becomes possible—something that would not be achievable with the Pi 4 without the accelerator.

Thanks to Python and numerous online examples around TensorFlow, getting started in artificial intelligence and machine learning with the USB Accelerator is easy and stylish.

Here you can find the official "Get Started" guide for the USB Accelerator!

Technical Specifications of the USB Accelerator

High-performance 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: USB Stick: 65 mm x 30 mm x 8 mm

These benchmarks provide insight into the performance capabilities of the USB Accelerator.

Host System Requirements

Linux Debian 6.0 or higher, or a derivative (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 (USB 3 recommended for best performance)

Python 3.5, 3.6, or 3.7

Operating Temperature

Recommended operating temperature:

35°C - reduced clock frequency

25°C - maximum clock frequency (for optimal performance)

Included in the USB Accelerator Package

USB Accelerator

USB 3 cable

For optimal use with the Pi 4, we have assembled a development kit that we recommend to all users:

Included in the USB Accelerator Development Kit

USB Accelerator

USB 3 cable

Raspberry Pi 4 (8 GB)

FLIRC case (for optimal passive cooling of the Pi 4 / 8 GB)

USB C 3A power supply (EU, white - US/AUS/UK available on request)

32 GB microSD card with NOOBS / Raspbian Buster

2 x microHDMI to HDMI cable (1 m, Raspberry Pi Foundation)

2 m CAT 6 LAN cable (optimal for Gigabit)

Note: Additional software is required to operate the USB Accelerator, which must be installed separately—it is not included on the SD card.

The company behind this technology provides several interesting examples and tutorials in the project, including a "variant" of a popular AI game. (This game defeated human players in a game considered extremely complex and significantly more challenging for computers than, for example, chess.)

Potential for Industrial Applications & Consulting The USB Accelerator is a revolutionary product, similar to the Raspberry Pi, for machine learning applications! It enables embedded solutions that can, for example, recognize issues with workpieces, assess traffic situations, and much more.

We see great potential, especially in combination with the new Raspberry Pi HQ camera.

Notes &