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Google Coral Edge TPU items are available at Gravitylink!

Great news! All Google Coral items are available at #Gravitylink online store!
✅Mini PCIe Accelerator: $34.99
✅M.2 Accelerator A+E key: $34.99
✅M.2 Accelerator B+M key: $34.99
✅Environmental Sensor Board: $24.99
✅System-on-Module (SoM): $114.99



Mini PCIe Accelerator: $34.99
A PCIe device that enables easy integration of the Edge TPU into existing systems.
Integrate the Edge TPU into legacy and new systems using a Mini PCIe interface.

The Coral Mini PCIe Accelerator is a PCIe module that brings the Edge TPU coprocessor to existing systems and products.
The Mini PCIe Accelerator is a half-size Mini PCIe card designed to fit in any standard Mini PCIe slot. This form-factor enables easy integration into ARM and x86 platforms so you can add local ML acceleration to products such as embedded platforms, mini-PCs, and industrial gateways.

M.2 Accelerator A+E key: $34.99
M.2 Accelerator B+M key: $34.99

Integrate the Edge TPU into legacy and new systems using an M.2 A+E key/M.2 B+M key interface.

The Coral M.2 Accelerator is an M.2 module that brings the Edge TPU coprocessor to existing systems and products.

The M.2 Accelerator is a dual-key M.2 card (either A+E or B+M keys), designed to fit any compatible M.2 slot. This form-factor enables easy integration into ARM and x86 platforms so you can add local ML acceleration to products such as embedded platforms, mini-PCs, and industrial gateways.

Environmental Sensor Board: $24.99
An accessory board that provides temperature, light and humidity sensors for IoT applications.
The Environmental Sensor Board is an add-on board (also known as a pHAT or bonnet) that adds sensing capabilities to your Coral Dev Board or Raspberry Pi projects. It includes a secure cryptoprocessor with Google keys to enable connectivity with Google Cloud IoT Core services, allowing you to securely connect to the device and then collect, process, and analyze the sensor data.

System-on-Module (SoM): $114.99
A fully-integrated system for accelerated ML applications in a 40mm x 48mm pluggable module.

The Coral SoM is a fully-integrated Linux system that includes NXP's iMX8M system-on-chip (SoC), eMMC memory, LPDDR4 RAM, Wi-Fi, and Bluetooth, and the Edge TPU coprocessor for ML acceleration. It runs a derivative of Debian Linux we call Mendel.

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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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