05 May Edge AI – What is it and how does it Work?
Edge AI is a combination of Edge Computing and Artificial Intelligence. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location of the device.
Artificial Intelligence algorithms process the data that are created on the device with or without having any internet connection. This allows data to be processed within a few milliseconds providing real-time feedback.
Edge AI is a system that uses Machine Learning algorithms to process data generated by a hardware device at the local level.
How Edge AI Works?
In Edge AI the machine learning algorithms run directly at the edge. In edge computing, the data and information are processed directly at the IoT device or machine, i.e., the edge. Edge computing is rapidly growing because of its inherent advantages: Real-Time Analytics, Reduced Latency, High Speed, etc.
In Edge AI systems the machine learning algorithms can run on existing CPUs pr even less capable microcontrollers (MCUs) in edge devices. When compared to other applications that utilize highly efficient AI chips, Edge AI provides superior performance while also reducing power consumption.
What is Edge AI Software?
Edge AI software is large number of machine learning algorithms that run on a physical hardware device. The idea is to run AI algorithms on a local device or machine. Edge AI software allows users to get data in real-time because it does not need other systems or internet connections to connect to others.
What is Edge AI Hardware?
Edge AI can run on a wide range of hardware platforms from normal MCU to advanced neural processing devices. IoT devices and machines are examples of Edge AI hardware devices.
Edge AI-connected devices use embedded algorithms to monitor device behaviour and to collect and process the device data. The devices will make decisions, automatically correct the problems and make future performance predictions. This is all executed without human involvement.
Smartphones, laptops, Smart Driven cars, Raspberry PI’s are also good examples of Edge AI devices.
Advantages of Edge AI – Applying Machine Learning on Edge
- Reduced latency: Transfer of data back & forth from the cloud takes time. Edge AI reduces latency by processing data locally (at the device level).
- Real-time analytics: Real-time analytics is a major advantage of Edge Computing. Edge AI brings high-performance computing capabilities to the edge, where sensors and IoT devices are located.
- Higher speeds: Data is processed locally which significantly improves processing speed as compared to cloud computing
- Reduced bandwidth requirement and cost: Edge AI processes the data locally on the device itself, reducing the cost of internet bandwidth and cloud storage.
- Improved data security: Edge AI systems perform the majority of data processing locally i.e. on the edge device itself. This greatly reduces the amount of data that is sent to the cloud and other external locations. This eliminates the exposure of sensitive data to cyber-criminals.
- Scalability: Edge AI typically processes large amounts of data. If you have to process video image data from many different sources simultaneously, transferring the data to a cloud service is not required.
- Improved reliability: Higher levels of security combined with greater speed produce greater the reliability of Edge AI System.
- Reduced cost: AI processing is working on the edge of the device so it is highly cost-cost efficient because only processed, data required or valuable data is sent to the cloud.
- Reduced power: Edge AI processes data at the device level so it saves energy costs
Cloud Computing AI Vs Edge AI
As opposed to cloud-based AI solutions where all data is processed and stored within a cloud environment an Edge AI system processes all devices and machine data at the asset level (at the extreme edge). A device using Edge AI does not need to be connected to the internet in order to work properly, it can process data/information and make decisions independently without a connection. The application of Edge AI only requires a device with a microprocessor or sensors.
Cloud computing allows organizations to train large-scale models very quickly
Cloud computing is good and suitable for model training but may be challenging for different inferences – provides predictions in response to users’ actions & queries.
Several challenges raised using the cloud for inference:
- AI use cases need real-time responses from the devices they are monitoring. Cloud-based inference cannot provide this real-time response due to inherent issues with latency.
- If edge devices have connectivity issues or no internet connection it can not perform well. Sufficient bandwidth required to transfer the relevant amount of data in a proper timeframe can also be an issue.
In Edge AI, the AI models operate on the devices themselves, without the need of an internet connection and without the problems associated with data latency. This produces much faster data processing and supports use cases that require real-time inferencing.
However, Edge AI systems can have a few challenges since their AI models must also have several issues because these models need to be trained on a regular ongoing basis using data from the edge devices:
- In this case, the system needs to create a dataset by transferring data from a huge number of edge devices to the cloud. Maybe this can be complex and hard to achieve, depending on the bandwidth available & connectivity to the edge devices.
- As defined this system need to create a database and storing all the data in a central location creates security and privacy issues. In some cases, it is difficult to train AI models based on end-user data, and a centralized database represents a security risk.
Edge AI use cases and industry examples
Manufacturing: – Edge AI provides rapid collection and analysis of data produced by edge-based devices and sensors. This allows manufacturers to execute better control of critical assets and to implement predictive maintenance protocols.
Energy (Oil and Gas): Generally, oil and gas plants are situated in remote locations. The powerful feature of edge computing like real-time analytics with information processing is on assets themselves, means the requirement of good quality connectivity is very less.
Industrial IoT: Edge AI can be used to automate the assembly line, and AI to visually inspect products for defects. Inspection of devices/machines is done via AI algorithms instead of human beings performing manual inspections can save time & money.
Autonomous Vehicles: Edge AI can be deployed in autonomous vehicles where real-time analysis is critical. Without real-time data processing, autonomous vehicles would not be possible. If autonomous vehicles have to rely on the cloud for data processing that could take seconds to perform, collisions would increase because milliseconds matter when operating a vehicle.
Healthcare (Patient Monitoring): Edge AI applications in the healthcare (patient monitoring) sector provide several distinct advantages when compared to a traditional cloud-based system. Generally, in hospitals, the monitoring of devices like glucose monitors, cardiac trackers, blood pressure sensors, etc. are either not connected, or where they are, large amounts of unprocessed data from devices need to be stored in a cloud environment or on multiple servers. An Edge AI application allows the healthcare provider to process all patient monitoring device data locally. Edge AI also enables real-time analytics to record patient behaviours and view patient dashboards for full visibility.
Smart Homes: Smart homes rely only on IoT devices that collect and processes data from the house. Then this data is sent to a centralized remote server, where it is processed and stored. So this architecture may have the problem of higher cost, security/privacy risks & latency. By using Edge AI data movement time can be reduced and also the sensitive information can be processed only on edge