跳至主要内容

How can I get a Google Coral mini dev board engineering prototype?



Google announced Coral new devices: Coral Accelerator Module an easy to integrate multi-chip package that encapsulates the Edge TPU ASIC. The module exposes both PCIe and USB interfaces and can easily integrate into custom PCB designs, and Coral Dev Board Mini, which provides a smaller form-factor, lower-power, and lower-cost alternative to the Coral Dev Board.

It’s said the two new hardware will be released at the first half of 2020. I just wonder is there any chance to get an engineering prototype?

I've tried to contact with Google’s Coral distributor Gravitylink, and they have neither.Is there any way to get one?

About Google New Coral Products:

Last year, Google launched Coral, our platform of hardware components and software tools that make it easy to prototype and scale local AI products. Our product portfolio includes the Coral Dev Board, USB Accelerator, and PCIe Accelerators, all now available in 36 countries.

Since it releases, Google were excited by the diverse range of applications already built on Coral across a broad set of industries that range from healthcare to agriculture to smart cities. And for 2020, we’re excited to announce new additions to the Coral platform that will expand the possibilities even further.

First up is the Coral Accelerator Module, an easy to integrate multi-chip package that encapsulates the Edge TPU ASIC. The module exposes both PCIe and USB interfaces and can easily integrate into custom PCB designs.


Coral Accelerator Module, a new multi-chip module with Google Edge TPU
The Coral Dev Board Mini, which provides a smaller form-factor, lower-power, and lower-cost alternative to the Coral Dev Board. The Mini combines the new Coral Accelerator Module with the MediaTek 8167s SoC to create a board that excels at 720P video encoding/decoding and computer vision use cases. 

Google Coral Edge TPU serial products are available at global distributor Gravitylink online store, go for here for more details: https://store.gravitylink.com/global/

标签：

评论

发表评论

此博客中的热门博文

10 questions you should know about Google Edge TPU

1. What is the Edge TPU? The Edge TPU is a small ASIC designed by Google that provides high performance ML inferencing for low-power devices. For example, it can execute state-of-the-art mobile vision models such as MobileNet V2 at almost 400 FPS, in a power efficient manner. Google offers multiple products that include the Edge TPU built-in. Two Edge TPU chips on the head of a US penny 2. What machine learning frameworks does the Edge TPU support? TensorFlow Lite only. 3. What type of neural networks does the Edge TPU support? The first-generation Edge TPU is capable of executing deep feed-forward neural networks (DFF) such as convolutional neural networks (CNN), making it ideal for a variety of vision-based ML applications. 4. How do I create a TensorFlow Lite model for the Edge TPU? You need to convert your model to TensorFlow Lite and it must be quantized using either quantization-aware training (recommended) or full integer post-training quantization. (To create a compatible model...
发表评论
阅读全文

How to Retrain an object detection model

This tutorial shows you how to retrain an object detection model to recognize a new set of classes. You'll use a technique called transfer learning to retrain an existing model and then compile it to run on an Edge TPU device—you can use the retrained model with either the Coral Dev Board or the Coral USB Accelerator. Specifically, this tutorial shows you how to retrain a MobileNet V1 SSD model (originally trained to detect 90 objects from the COCO dataset) so that it detects two pets: Abyssinian cats and American Bulldogs (from the Oxford-IIIT Pets Dataset). But you can reuse these procedures with your own image dataset, and with a different pre-trained model. The steps below show you how to perform transfer-learning using either last-layers-only or full-model retraining. Most of the steps are the same; just keep an eye out for the different commands depending on the technique you desire. Note: These instructions do not require deep experience with TensorFlow o...
发表评论
阅读全文

Introducing Google Coral Dev Board

Introducing Google Coral Dev Board (Part 1) Introducing Google Coral Dev Board (Part 2) Introducing Google Coral Dev Board (Part 3) Introducing Google Coral Dev Board (Part 4)
发表评论
阅读全文