跳至主要内容

Introducing Google Coral Edge TPU Device--Mini PCIe Accelerator

Mini PCIe Accelerator
A PCIe device that enables easy integration of the Edge TPU into existing systems.
  • Supported host OS: Debian Linux
  • Half-size Mini PCIe form factor
  • Supported Framework: TensorFlow Lite
  • Works with AutoML Vision Edge
https://store.gravitylink.com/global/product/miniPcIe
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.
https://store.gravitylink.com/global/product/miniPcIe

Features

  • Google Edge TPU ML accelerator
  • Standard Half-Mini PCIe card
  • Supports Debian Linux and other variants on host CPU

About Edge TPU 

The Edge TPU is a small ASIC designed by Google that provides high performance ML inferencing with low power requirements: it's capable of performing 4 trillion operations (tera-operations) per second (TOPS), using 0.5 watts for each TOPS (2 TOPS per watt). 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. This on-device processing reduces latency, increases data privacy, and removes the need for constant high-bandwidth connectivity.

https://store.gravitylink.com/global/product/miniPcIe

Requirements

The Coral Mini PCIe Accelerator must be connected to a host computer with the following specifications:
  • Any Linux computer with a compatible Mini PCIe module slot
    • Debian 6.0 or higher, or any derivative thereof (such as Ubuntu 10.0+)
    • System architecture of either x86-64 or ARM32/64 with ARMv8 instruction set

Thermal limit and operating frequency

The thermal resistance and max allowed temperature of the Edge TPU stack-up is as follows:
  • Thermal resistance (junction to top of shield can): 11 °C/W
  • Maximum Edge TPU junction temperature: 100 °C
The Mini PCIe Accelerator does not include a thermal solution to dissipate heat from the system. In order to sustain maximum performance from the Edge TPU, it's important that you design your system so the Edge TPU operates well below the maximum Edge TPU temperature. If the Edge TPU gets too hot, it slowly reduces the operating frequency and may reset to avoid permanent damage.

Software and operation
The host system must be running Debian Linux 6.0 or higher, or any derivative thereof, and have the Edge TPU runtime and API library installed.

https://store.gravitylink.com/global/product/miniPcIe

If you are interested in Google Coral products, and more details about large volume or bulk sales (Volume Discounts) at #Gravitylink online store, 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. 
标签：

评论

发表评论

此博客中的热门博文

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

How to retrain an image classification model?

Got a tutorial from Google Coral Team: This tutorial shows you how to retrain an image classification 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  quantized  MobileNet V1 model to recognize different types of flowers (adopted from TensorFlow's docs). But you can reuse these procedures with your own image dataset, and with a different pre-trained model. Tip:  If you want a shortcut to train an image classification model, try Cloud AutoML Vision. It's a web-based tool that allows you to train a model with your own images, optimize it, and then export it for the Edge TPU. Set up the Docker container Prepare your dataset Retrain your classification model Compile the model for the Edge TPU Run...
发表评论
阅读全文