Coral Accelerator Module – Single Pack

£19.20 incl. VAT

Coral Accelerator Module – Single Pack

Coral Accelerator Module
Coral Accelerator Module
Coral Accelerator Module

Coral Accelerator Module – Single Pack

The Coral Accelerator Module is designed to perform high-speed inferencing for machine learning (ML) models. The Coral module is a multi-chip module (MCM) that includes the Edge TPU ML accelerator with integrated power control, which can be connected over a PCIe Gen2 x1 or USB2 interface. The on-device ML processing reduces latency, increases data privacy, and removes the need for a constant internet connection. More

£19.20 incl. VAT
£16.00 excl. VAT

In stock

Product Details

Description

The Coral Accelerator Module is designed to perform high-speed inferencing for machine learning (ML) models. The Coral module is a multi-chip module (MCM) that includes the Edge TPU ML accelerator with integrated power control, which can be connected over a PCIe Gen2 x1 or USB2 interface. The on-device ML processing reduces latency, increases data privacy, and removes the need for a constant internet connection.

Details

Key features of the Coral Accelerator Module

  • Google Edge TPU ML accelerator
    • 4 TOPS peak performance (int8)
    • 2 TOPS per watt
  • Integrated power management
  • PCIe Gen2 x1 or USB 2.0 interface
  • Surface-mounted (LGA) module
  • RoHS compliant

Coral Accelerator Module functional block diagram

Coral Accelerator Module

 

Why should you buy the Coral Accelerator Module?

The Coral module performs high-speed ML inferencing

The Edge TPU accelerator is a small ASIC designed by Google that accelerates TensorFlow Lite models in a power-efficient way. It is designed to perform 4 trillion operations per second (4 TOPS), using 2 watts of power—that’s 2 TOPS per watt. 

For example, one Edge TPU can execute state-of-the-art mobile vision models such as MobileNet v2 at almost 400 frames per second. 

The Coral module supports TensorFlow Lite

The Coral Accelerator Module is a surface-mounted module that includes the Edge TPU and its own power control. It provides accelerated inferencing for TensorFlow Lite models on your custom PCB hardware.

There is no need to build models from the ground up. TensorFlow Lite models can be compiled to run on the Edge TPU.

The Accelerator Module is:

  • Small enough to fit into almost any design (size: 15.0 x 10.0 x 1.5 mm).

  • Lets developers solder the Edge TPU acceleration onto a PCB.

  • Saves even more space due to a lack of connectors or cables.

  • Requires no exotic boards or surface mounts, thanks to conventional rather than high-density interconnector parts.

  • Provides a standard USB 2.0 or PCIe interface to an application processor.

  • Allows developers to add a single system component to their designs to enable ML acceleration.

 

Technical Specifications

Dimensions: 15.0 x 10.0 x 1.5 mm
Weight: 0.67 g
Operating temperature: -40 to +85 °C
Chipset: Google Edge TPU and PMIC
Mounting type: SMT, 120-pin LGA
Serial interface: PCIe Gen 2 or USB 2.0

Note: USB 3.0 is also available but requires special design considerations and support

 

Specifications

SKU

2154732

mpn

G313-06329-00

brand

Coral

Reviews

Okay, no reviews ... yet.

Be the first to review Coral Accelerator Module - Single Pack. Help others in the community by sharing your experience.

We are here to help you design the world. This product is covered by our refunds policy.

Add a review

This will show next to your review. You can use a pseudonym if you want to.
This is used to check our reviews are written by real people. Anyone can write a review. Your review will show as written by a 'Verified owner' if you've bought this product from okdo.com using this email before. It will never be sold or used to send you marketing emails.

Technical Reference

Location

Please select an option to see content specific to your location and shop online.

Browse the US site

Privacy

Our website uses cookies and similar technologies to provide you with a better service while searching or placing an order, for analytical purposes and to personalise our advertising. You can change your cookie settings by reading our cookie policy. Otherwise, we’ll assume you’re OK with our use of cookies.