Learn how to use Edge TPU with TensorFlow to improve the performance of your machine learning models on Arm-based devices.
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This document is a guide to using Edge TPU with the TensorFlow framework. Edge TPU is a purpose-built processor designed to run neural networks efficiently on devices at the edge, such as mobile phones and embedded systems. With Edge TPU, you can run state-of-the-art neural networks such as MobileNet v2 at high speed on devices with very limited computational resources.
What is Edge TPU?
Edge TPU is a co-processor for low-power devices that provides high performance ML inferencing. It is designed to run TensorFlow Lite models and is used by Google AIY Vision and Voice kits, as well as the Coral Dev Board and USB Accelerator.
Why use Edge TPU with TensorFlow?
Edge TPU is a special purpose accelerator for machine learning, designed to run deep neural networks on devices with very limited computational power. It is particularly well suited for running on low-power embedded devices such as the Raspberry Pi.
One of the advantages of using Edge TPU is that it allows you to run inference on your devices without needing to send data back to the cloud. This can be important for privacy reasons, or if you have devices that are not always connected to the internet.
Another advantage of using Edge TPU is that it can provide a significant speedup compared to running inference on a CPU or GPU. This is because Edge TPU is designed specifically for neural network inference, and can therefore make better use of limited computational resources.
If you’re interested in using Edge TPU with TensorFlow, there are a few things you need to know. In this article, we’ll first look at how to install Edge TPU and get started with the basic commands. Then we’ll go through a short example showing how to use Edge TPU with a pre-trained model.
How to use Edge TPU with TensorFlow?
If you’re a developer who wants to use the Edge TPU in your own applications, this guide shows you how to use the Edge TPU with the TensorFlow Lite Converter. The TensorFlow Lite Converter takes a model created with the TensorFlow framework and optimizes it for execution on an Edge TPU.
Advantages of using Edge TPU with TensorFlow
TensorFlow is a powerful open-source software library for data analysis and machine learning. Edge TPU is a custom accelerator chip designed by Google that dramatically speeds up machine learning workloads. When used together, TensorFlow and Edge TPU can provide significant performance boosts for both training and inference.
There are many advantages to using Edge TPU with TensorFlow. First, Edge TPU is highly optimized for TensorFlow Lite, which means that you can get the most out of your Edge TPU hardware when using TensorFlow Lite. Secondly, the Edge TPU integration withTensorFlow lets you take advantage of Google Cloud Platform services such as AutoML Vision and Cloud Functions. Finally, using Edge TPU with TensorFlow can help you deploy your models on resource-constrained devices such as Android smartphones and Raspberry Pi boards.
Disadvantages of using Edge TPU with TensorFlow
There are a few disadvantages of using Edge TPU with TensorFlow. One downside is that it can be difficult to install and set up. Also, Edge TPU only works with specific types of models and data, so you may need to convert your models or data to a compatible format. Additionally, Edge TPU can be slower than other options when running inference on large models.
In this guide, you’ve learned how to use the Edge TPU with TensorFlow to accelerate inferencing on edge devices. You’ve also seen how to retrain a TensorFlow model to tailor it for the Edge TPU. Now you’re ready to start using the Edge TPU for your own applications.
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