Introduction to 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 architecture is very beneficial in manufacturing, surveillance, and monitoring industries. Read more about Edge AI Applications and How does it work
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 Maps. 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 applications like Google Alexa and Apple Siri by sending voice recordings to an edge network, and voice recording is processed by passing text via AI.
What is the Edge AI Architecture?
Here are the three layers on which Edge AI is constituted:
IoT layer
The IoT layer is embedded in IoT devices such as mobiles, smart cars, smart fridges, sensors, actuators, and controllers to monitor objects in services, human activities, or operations. 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 update 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, i.e., interactive interfaces, play a significant part; they are mainly used for providing a more complex set of qualities. It is the source for workloads, which need to handle processing. It is responsible for visualization, machine learning, artificial intelligence, and data analytics.
What is Edge AI Stack?
Following is the stack that every organization must have for the right implementation of Edge AI
Custom Design Services
The ecosystem of design services helps us provide broad market applications like smart cars, devices, and cities.
Reference Designs/Demos
In mobiles, we use face detection to unlock our phones. Speed breaker detection, voice recording, face detection, sensors, and object counting are examples.
Software Tools
This tells us the software we are using for face detection or voice recording, such as a Neural network compiler tool for Caffe/TensorFlow to FPGA.
IP Cores
This tells us which IP cores we are using for face detection, voice recording, or keyphrase detection. Examples of IP cores are Convolutional Neural Network (CNN) accelerators and Binarized Neural Network (BNN) accelerators.
Modular Hardware Platforms
The most popular hardware platforms are the award-winning Embedded Vision Development Kit and the iCE40 UltraPlus device-based Mobile Development Platform (MDP). These platforms describe hardware embedded in our devices for edge computing.
Explainable AI in manufacturing improves efficiency, workplace safety, and customer satisfaction by automating their tasks. Click to explore our, Explainable AI in Manufacturing Industry
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 was transferred to the cloud, containing raw video signals, which were continuously streaming to the cloud server. The large volume of video footage moved to the cloud caused 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 does not 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 be sent over the internet to the Amazon Alexa voice service that helps in phrasing voice into commands 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 by the central controller, process it 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, and Supporting the Diagnosis of Neurological Diseases. This helps doctors make faster decisions 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 in the future is more efficient and effective. It will require AI for visual inspection of 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 quickly.
Implementing Edge AI in Edge Computing is because of its flexibility and enabling smart devices to support different industries. Explore here about Bringing AI at the Edge
Future of Edge AI
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Edge AI reduces cost and latency times to improve user experience. Most organizations understand edge AI's importance and integrate this technology into devices and provide users with faster and more efficient service. This will help them grab more attention from customers and demand for their product in the market.
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Edge AI increases security by processing data locally, and there will be no need to transfer data to the cloud server.
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Data need not be transferred to the cloud server, so reducing bandwidth will help reduce the cost of contracted internet service.
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The demand for autonomous technology is increasing day by day. Edge technology devices do not require special maintenance through data scientists or AI developers.
Conclusion
Edge AI offers almost endless possibilities. IoT devices are a great use of edge AI. Edge AI will allow real-time operations, including data creation, decision-making, and action where milliseconds matter. Real-time functions are essential for self-driving cars, robots, and many other areas.
- Read about Edge AI vs Federated Learning
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