跳至主要内容

Google Coral Serial Products are in stock at Gravitylink!

Google Coral provides a complete platform for accelerating neural networks on embedded devices. At the heart of our accelerators is the Edge TPU coprocessor. It's a small-yet-mighty, low-power ASIC that provides high performance neutral net inferencing.



For startups and large-scale enterprises: 
Google Coral Products flexible for prototyping and production

Prototype
Imagine what’s possible with local AI, then move right into proof-of-concept. Google Coral Edge TPU devices make it easy to take your idea from sketch pad to workbench.

Scale
Google Coral Edge TPU devices are designed to grow with you, integrating seamlessly into production processes at any scale.

Deploy
Robust modular components bring reliable AI to more applications than you ever dreamed possible.

Coral is a complete toolkit to build products with local AI. Our on-device inferencing capabilities allow you to build products that are efficient, private, fast and offline.




Google Coral all hardware is available at Gravitylink online store: https://store.gravitylink.com/global

Gravitylink is a global volume distributor of Google Coral.


If you are interested in Google Coral products, and more details about large volume or bulk sales (Volume Discounts) , welcome to contact our sales team via email: sales@gravitylink.com or market@gravitylink.com, and we'll get back to you with our best quotation ASAP. 


Coral’s local AI technology enables new possibilities across almost any kind of industry

Coral for smart cities: Building smarter cities


In order to reach the true potential of a “smart city,” AI processing will have to move from the cloud onto secure local devices, maintaining individual privacy, reducing data transfer rates, and enabling quicker reaction times for critical systems.

Coral for industry & manufacturing: Making manufacturing work better


Edge AI use cases in industries are wide ranging, from quality control in manufacturing lines to safety monitoring of human-machine interaction. These applications need fast, low latency inference without compromising on accuracy.

Coral for automotive: Helping people get around



Local AI not only helps vehicles drive more safely, it is also used to ensure driver attentiveness, provide seamless control of on-board systems, and verify the smooth operation of countless moving parts under the hood.

Coral for healthcare: Keeping people healthy


From patients to providers, local AI can help reduce healthcare costs by enabling more accurate diagnoses, reducing preventable accidents, and improving compliance. Edge devices can also extend healthcare to underserved populations where medical infrastructure might not exist.

Coral for agriculture: Feeding the world’s population


Local AI provides high performance, offline analysis for agriculture challenges that can improve soil quality, plant health, and crop yield in order to produce more food, reduce environmental impact, and enable sustainable farming practices.


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...

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...

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)