Edge computing and machine learning technology can be costly,difficult to understand,and difficult to operate.



<h2>Article</h2>



<figure class="wp-block-image is-style-default"><img src="https://www.androidauthority.com/wp-content/uploads/2015/07/machine-learning-ai-artificial-intelligence-e1462471461626.jpg" alt="AI "/></figure>



Edge computing and machine learning applications require dedicated hardware,which can be costly,difficult to understand,and difficult to operate.



Using a familiar board,such as the <a href="https://eduengteam.com/2021/07/50-raspberry-pi-hacks-tips.html" data-type="post" data-id="3722">Raspberry Pi</a> or an Arduino (which most of us already have),and simply extending it with a HAT or a shield is a terrific approach to start into AI. Adding a HAT or shield to a single-board computer increases its processing capabilities (typically at a lower cost),making it more effective in machine learning and edge computing applications.



Here I look at software solutions that allow edge computing and machine learning by an FPGA unit or Raspberry Pi model.



<h2>Grove AI Raspberry Pi HAT</h2>



The Grove AI HAT is most likely the first item you'll come across while looking for an AI HAT for Raspberry Pi. I believe it has something to do with the HAT's incredibly low price and what the manufacturer,Seeed Studio,claims it can achieve.



The HAT claims to assist the Raspberry Pi in a variety of AI-on-the-edge applications,ranging from maker projects to commercial medical devices,for around $29USD. Additionally,the board can be used as an independent Arduino capable of running AI applications.



<figure class="wp-block-image is-style-default"><img src="https://maker.pro/storage/75yAlEy/75yAlEyKN9Q5ZnX907jOFiFZq1gqmKw5znEdBSIr.jpeg" alt="the seeed grove ai hat"/><figcaption>Seeed Studio's Grove AI HAT. Image courtesy of <a href="https://www.seeedstudio.com/Grove-AI-HAT-for-Edge-Computing-p-4026.html" target="_blank" rel="noreferrer noopener">Seeed Studio</a>.</figcaption></figure>



However,<a href="https://www.tomshardware.com/reviews/seeed-studio-grove-ai-hat-raspberry-pi,6336.html" target="_blank" rel="noreferrer noopener">according to one review</a>,that is exactly what the Seeed Grove AI HAT is:



an Arduino development board featuring GPIO pins that can communicate with the Raspberry Pi It can't read the camera on the Raspberry Pi or use a display connected to it.



For the module to work,you'll need to purchase a separate camera and display,which adds about $14 to the price. The HAT is also poorly described,making it difficult to write software that utilizes it.



<h2>DepthAI Raspberry Pi HAT</h2>



DepthAI is a platform that offers real-time depth vision and object detection to the Raspberry Pi,according to the manufacturer Luxonis.



Because the DepthAI is based on the Myriad X and includes a simple,easy-to-use Python API,it may be used for a wide range of image processing and filtering applications while reducing the Raspberry Pi's CPU load.



<figure class="wp-block-image is-style-default"><img src="https://maker.pro/storage/WbOBcCq/WbOBcCq3ER9j0iscG0MkKOmnzPFY7B0CRxKkbAy2.jpeg" alt="image1.jpg"/><figcaption>The DepthAI comes in various sizes,one of which is a HAT for the Raspberry Pi. Image courtesy of <a href="https://luxonis.com/depthai" target="_blank" rel="noreferrer noopener">Luxonis</a>.</figcaption></figure>



This method may be used to detect objects and their locations in 3D space right out of the box. The open-source project also appears to be adequately described.



The DepthAI is now available in a variety of kits and models. One of them is a $149 USD HAT for the latest Raspberry Pi models.



<h2>Coral TPU USB Accelerator</h2>



If you're working with TensorFlow in your machine learning project,the Coral TPU USB accelerator is a viable solution. A Tensor processing unit (TPU) is a neural network machine learning accelerator circuit designed by TensorFlow.



Machine learning models for edge computing are run on an Edge TPU processor. As a result,it's significantly smaller and uses a lot less energy.



As long as the computer has a USB port,the USB accelerator may provide a TPU as a coprocessor for any modern computer running Windows,Linux,or macOS. So,while it isn't exactly a Raspberry Pi HAT,it is totally compatible with it.



<div class="wp-block-image is-style-default"><figure class="aligncenter"><img src="https://maker.pro/storage/6JJrEiQ/6JJrEiQeakn4ZXYOyZe2dLuCgitICZ6sisupBjQ8.jpeg" alt="coral tpu usb accelerator"/><figcaption>The Coral TPU USB accelerator. Image courtesy of <a href="https://coral.ai/docs/accelerator/get-started/" target="_blank" rel="noreferrer noopener">Coral</a>.</figcaption></figure></div>



The runtime,used to execute models on this device,is based on TensorFlow Lite,so the accelerator needs to be trained using <a href="https://github.com/tensorflow/tensorflow/tree/r1.13/tensorflow/contrib/quantize" target="_blank" rel="noreferrer noopener">quantization-aware training</a>.



The Coral USB Accelerator is developed and maintained by Google which means the documentation is fantastic,and you can expect to receive support when you need it. The official documentation clearly explains <a href="https://coral.ai/docs/accelerator/get-started/" target="_blank" rel="noreferrer noopener">how to set the device up and get started</a>.



<h2>Spartan Edge Accelerator Board for Arduino</h2>



I've only talked about Raspberry Pi HATs so far. Similar products are available for Arduinos as well. Another device produced by Seeed Studio is the <a href="https://www.seeedstudio.com/Spartan-Edge-Accelerator-Board-p-4261.html" target="_blank" rel="noreferrer noopener">Spartan Edge Accelerator Board</a>,.



This shield,like the  <a href="https://maker.pro/arduino/tutorial/getting-started-with-fpgas-using-the-arduino-mkr-vidor-4000" target="_blank" rel="noreferrer noopener">MKR Vidor 4000</a>,adds an FPGA to an Arduino,and its main purpose is to conduct filtering operations and link the Arduino to a network. It also has an onboard ESP32,which is responsible for connecting the Arduino to various networks.

Consider Edge Computing and AI HATs and Shields when you're thinking about chip implants and personal access points.

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Consider Edge Computing and AI HATs and Shields when you're thinking about chip implants and personal access points.

Consider Edge Computing and AI HATs and Shields when you're thinking about chip implants and personal access points.

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