Tag: Coprocessor

Price: $89.00
(as of Jan 03,2025 12:55:02 UTC – Details)

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

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

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

AI for Everyone: Google has connected a powerful specialized chip (TPU, Tensor Processing Unit) to a USB 3 interface with the Coral 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).)

Google 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, Google Maps improves by analyzing street signs captured by Street View using a TensorFlow-based neural network. The bonus: TensorFlow can easily be programmed in Python.

AI for Home Use? Yes! Google is an interesting company—they don’t keep this technology to themselves but share it with the world. Early last year, Google launched a USB 3 stick with the Edge TPU, which supports the TensorFlow Lite framework. The Edge 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 Google Coral Edge 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 Google Coral USB Accelerator is easy and stylish.

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

Technical Specifications of the 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

These benchmarks provide insight into the performance capabilities of the Coral 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 Google Coral 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 Coral 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 Coral USB Accelerator, which must be installed separately—it is not included on the SD card.

Google provides several interesting examples and tutorials in the Coral.ai project, including a “variant” of AlphaGo Zero called Minigo. (AlphaGo Zero defeated human Go players in a game considered extremely complex and significantly more challenging for computers than, for example, chess.)

Potential for Industrial Applications & Consulting The Google Coral 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 & Miscellaneous

Important Note:

The USB stick can become very hot during operation, which can cause burns—please wait for it to cool down before handling!

Google and we accept no responsibility for damage if the device is operated outside the recommended temperature range.

• Google Part Number: G950-01456-01 // G950-06809-01 (starting October 2020)
• ASUS Part Number: 90AN0020-B0XAY0

Introducing the Coral USB Edge TPU ML Accelerator coprocessor – G950-01456-01/ G950-06809-01 for Raspberry Pi and Other Embedded Single Board Computers!

Are you looking to accelerate your machine learning projects on small form factor devices like the Raspberry Pi? Look no further than the Coral USB Edge TPU ML Accelerator coprocessor. This compact USB device packs a powerful punch, providing up to 4 TOPS (trillions of operations per second) of performance for deep learning inference tasks.

With the Coral USB Edge TPU ML Accelerator, you can run machine learning models efficiently on your Raspberry Pi or other embedded single board computers, enabling real-time processing of data at the edge. Whether you’re working on computer vision, natural language processing, or other AI applications, this coprocessor will help you achieve faster and more accurate results.

The Coral USB Edge TPU ML Accelerator is easy to set up and use, with support for popular machine learning frameworks like TensorFlow Lite and TensorFlow. It also integrates seamlessly with Google’s Coral ecosystem, providing access to a wide range of pre-trained models and tools for developing custom AI applications.

Don’t let limited compute power hold you back – supercharge your machine learning projects with the Coral USB Edge TPU ML Accelerator coprocessor. Order yours today and take your AI applications to the next level!
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other embedded single board computers