Complete Overview of Edge AI Implementation

Overview of Edge AI

People spend most of their time on mobile, gadgets, and different electronic devices in today's era. Organizations and developers understand the importance of deploying edge technology in devices to provide efficient and immediate services to their customers and increase their revenue. Edge Computing helps bring computation and data storage closer to the devices where it's being gathered; users need not rely on a central location. Mostly when we talk about real-time data, users will not face latency, bandwidth, and security issues, and that can affect an application's performance. Edge computing is growing exponentially due to the increase in IoT (Internet of Things) devices.


Edge AI and computing can raise innovation capabilities, operational excellence, and customer engagement - Gartner.


Edge AI states that running the machine learning algorithm locally on a hardware device using edge computing and AI algorithms is based on the data created on the device without requiring any connection. Like someone is asking Siri to call a particular person, asking Alexa to play a specific song, and asking for a roadmap for a particular location on google map. This allows users to process data with the device in less than 400 milliseconds, which gives you real-time information. Through Edge AI, the user communicates with the application like Google Alexa, Apple Siri by sending voice recording to an edge network, and voice recording is processed by passing text via AI. 


What is Edge Computing Architecture?

IoT layer

The IoT layer is embedded by IoT devices such as mobiles, smart cars, smart fridges, sensors, actuators, and controllers to monitor objects in services, human activities, or operation. Wireless standards like wifi are used in this layer.

Edge layer

This layer is in charge of analyzing data, computing, policy schedule, and orchestration and is also called the Edge Computing Architecture's central layer. It also helps monitor and updates the technological resources required to manage the organization's activities. This layer helps filter and preprocess data generated through the IoT layer in real-time. This data will then be sent to the next layer for Business Intelligence applications.

Business solution layer

This layer is made of business applications, authentication, and a set of services. Business application ecosystems have a significant part, i.e., Interactive interfaces; this is mainly used for providing a more complex set of qualities. It is the source for workloads, which need to handle the processing. It is responsible for visualization, machine learning, artificial intelligence, and data analytics.


What is Edge Computing Stack?

Custom Design Services

For broad market applications like a smart car, smart device, and smart cities, the ecosystem of design services helps us provide these applications.

Reference Designs/Demos

Like in mobiles, we use face detection to unlock our mobiles. Reference is speed breaker detection, voice recording, face detection, sensors, and object counting.

Software Tools

This tells the software we are using for face detection or voice recording. Like Neural network compiler tool for Caffe/TensorFlow to FPGA.

IP Cores

This tells us which IP cores we are using for our face detection or voice recording, or key phrase detection. These are examples of IP cores like Convolutional Neural Network (CNN) accelerator and Binarized Neural Network (BNN) accelerator.

Modular Hardware Platforms

The most popular hardware platforms are the award-winning Embedded Vision Development Kit and iCE40 UltraPlus device-based Mobile Development Platform (MDP). This tells about hardware that is embedded in our devices for edge computing.


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Use case of Edge AI.

These are major use cases for edge AI:-

Surveillance and Monitoring Purposes

Before Edge AI, the output created by security cameras is transferred to the cloud that contains raw video signals, and that signal was continuously streaming to the cloud server. The large volume of video footage moved to the cloud causes a heavy load on the cloud server's server.

Using edge AI, machine learning-enabled smart cameras can locally process captured images to spot and track multiple objects and other people and detect suspicious activities directly on edge. Camera footage needs not to transfer to the cloud server, reducing bandwidth, latency, and security issues. Now, servers can easily communicate with many cameras to minimize remote processing and memory requirements.

Smart Devices

Nowadays, almost everyone is familiar with face detection, face tracking Google Home, Alexa, and Apple Siri, and they all are using Edge AI. In this, words like Wake, To-Do list, and phrases such as "Alexa" have already been trained with a Machine Learning Model and processed locally on the speaker. Whenever it hears the word "wake," the word will send over the internet to the amazon Alexa voice service that helps in phrasing voice into command it understands. After processing, it will show you the desired output.

Autonomous Vehicles

Using Edge AI in autonomous vehicles or driverless cars, data is immediately processed within the same device, and action is performed within milliseconds. We need not send its data to a cloud server through edge AI and wait for a response. For autonomous vehicles, data should be immediately processed, like recognizing vehicles, traffic signs, pedestrians, roads, etc., to be able to operate safely. It is possible through Edge AI only. It helps identify all the information needed to the central controller, processes them immediately, and act accordingly.

Healthcare

Using edge AI in Healthcare helps in autonomous monitoring of hospital rooms, Identification of Cardiovascular Abnormalities, Detection of Fractures and Other Musculoskeletal Injuries, Supporting the Diagnosis of Neurological Diseases. This helps doctors make a faster decision in an emergency condition and provides the best treatment to their patients. This also increases patient satisfaction levels and helps hospitals remain competitive.

Industrial IoT

When it comes to manufacturing, automating a factory for the future is more efficient and effective. It will require AI from visual inspection for defects and robotic control for assembly. With Edge AI's help, you can deploy and develop AI capabilities at a low cost that can also process data at a fast speed.


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Future of Edge AI

  • Edge AI reduces cost and latency times to improve user experience. Most organizations understand edge AI importance and integrate this technology into devices and provide users faster and efficient service. This will help them grab more attention from customers and demand for their product in the market.
  • Edge AI increases security by processing data locally, and there will be no need to transfer data to the cloud server.
  • As data need not transfer to the cloud server. So a reduction in bandwidth will help in the reduction of the cost of contracted internet service.
  • The demand for autonomous technology is increasing day by day. Edge technology devices do not require special maintenance through data scientists or AI developers.

Future of Edge AI

As we can see in Figure 3, if we process data using cloud computing, it will have a latency minimum of 1084 milliseconds, and edge AI has a latency of only 357 milliseconds. That's why the demand for Edge AI is increasing exponentially.


Conclusion

There are almost endless possibilities of edge AI. IoT devices are a great use of edge AI. Edge AI will allow real-time operations, including data creation, decision, and action where milliseconds matter. Real-time functions are essential for self-driving cars, robots, and many other areas.