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How does Google Coral help human in our world?

Coral technology is enabling a new generation of intelligent devices
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.
How does Google Coral help human and make the world more better? Here are some examples:
✅Virgo uses AI to fight colon cancer
Colorectal cancer is the second most lethal cancer after breast cancer. Virgo uses Coral in its endoscopic video system to help doctors spot abnormalities in colonoscopies.
✅Olea Edge Analytics helps conserve water with Coral
Olea Edge, is helping water utilities and businesses conserve water by adding smarts to commercial water meters using Coral’s AI-at-the-edge platform.
✅Keeping loved ones safe with Coral and care.ai
Care.ai deploys computer vision to detect falls and other dangers. A vital factor in its success; Coral's AI platform, which keeps processing local, providing safety while preserving patient privacy.
Google Coral Performance
The Edge TPU is capable of performing 4 trillion operations (tera-operations) per second (TOPS), using 0.5 watts for each TOPS (2 TOPS per watt).
Flexibility and scalability
Google offers the Edge TPU in multiple form factors to suit various prototyping and production environments—from embedded systems deployed in the field, to network systems operating on-premise.
For example, our USB Accelerator simply plugs into a desktop, laptop, or embedded system such as a Raspberry Pi so you can quickly prototype your application. From there, you can scale to production systems by adding our Mini PCIe or M.2 Accelerator to your hardware system.
If you're looking for a fully-integrated system, you can get started with our Dev Board—a single-board computer based on NXP's i.MX 8M system-on-chip. Then you can scale to production by connecting our System-on-Module (included on the Dev Board) to your own baseboard.

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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...
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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...
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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)
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