跳至主要内容

Gravitylink Presents Google’s New Coral Products: Helping Enterprises Build Local AI Solutions



As the volume distributor of Google Coral, Gravitylink presents Google Coral Edge TPU all serial devices. Coral is a complete toolkit to help enterprises build products with local AI, providing hardware acceleration for neural networks … right on the edge device.

Due to its on-device capabilities with efficient, private, fast and offline, many enterprises have begun to use it to build diverse of applications range from healthcare, smart cities, transportation, to manufacturing, STEM education, etc.

To meet customers’ needs Coral offers kinds of devices from prototyping

(https://coral.ai/products/#prototyping-products/ ) to production, which is flexible enough for startups and large-scale enterprises.

Prototyping devices include Dev Board (Mini version will be released soon) and USB Accelerator. It’s easy to take prototype idea from sketch into proof-of-concept, with the help of these devices.

Production-ready devices include a system-on-module, 3 kinds of PCIe module, and a new product Accelerator Module to be released soon. These devices could integrate seamlessly into processes at any scale, helping create tailored solutions for industries.

With Edge TPU built inside, all these devices could bring high efficient ML inferencing to your products.

Besides, Google Coral also released 2 sensing products, a camera module and an Environmental Sensor Board.

As the volume distributor of Google Coral, Gravitylink has established a global sales network covering Asia, North America, Europe, and other countries and regions. Since it launches, a great number of enterprises have made bulk purchases of Coral devices at Gravitylink online store for production development.

For more details, please visit https://store.gravitylink.com/. Discounts are available for bulk purchases, moreover, transfer learning tools and various AI model resources will be offered with purchase of any product at Gravitylink.
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