Edge AI is a new computing paradigm that integrates artificial intelligence into cutting edge computing frameworks. Here are some of the benefits and use cases.
The adoption of edge computing has seen a great deal of growth in recent years. A recent report by Research and Markets states that the global edge computing market is expected to reach $155.90 billion by 2030.
Part of what has driven the growth of edge computing adoption in industries is artificial intelligence. With the increase in IoT and business data applications, there is a growing demand to develop devices that can handle information processing faster and smarter. This is where Edge AI technology comes in.
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The integration of AI into edge computing or the edge of high-end devices has made it possible to use AI algorithms to process information at the edge of the device or on a server near the device, reducing the time it takes for high-end devices to make computing decisions.
What is Edge AI?
Edge AI is the application of artificial intelligence to edge computing. Edge computing is a computing paradigm that allows data to be created and processed at the edge of the network rather than in a central data center. Therefore, Edge AI integrates artificial intelligence into high-end computing devices for faster and better data processing and intelligent automation.
Edge AI benefits
Data security and privacy
With the increasing number of recorded data accesses in recent years, many companies are looking for more ways to improve data privacy. Edge AI provides an enabling ground for data privacy because data processing activities take place at the edge of the device or near the device. As a result, the number of data sent to the cloud for the account has significantly decreased. In addition, when data is generated and processed in the same location, it increases the security and privacy of the data, making it more difficult for hackers to access your data.
Real time analytics
Real-time data processing has become vital due to the exponential growth of data generated by mobile devices and IoT devices at the edge of the network. Hence, one of the major benefits of edge AI is that it facilitates real-time data processing by ensuring high performance data computation on IoT devices.
This is possible because, with Edge AI, the data needed for an AI application is stored in high-end hardware in the device or a nearby server rather than in the cloud. This type of computing reduces latency in the computation and quickly returns the processed information.
Less internet bandwidth
The increasing amount of data generated by billions of devices around the world results in a massive need for Internet bandwidth to process data from cloud storage centers. This practice forces companies to allocate a huge amount of money to the purchase and subscription of bandwidth.
However, with edge AI, there is a significant reduction in the amount of bandwidth required to process information at the edge. In addition, because Edge AI calculates and processes data locally, fewer data is sent to the cloud over the Internet, thus saving a significant amount of bandwidth.
Less energy consumption
Maintaining a back-and-forth connection with cloud data centers consumes a significant amount of energy. As a result, many companies are looking for ways to lower energy bills, and edge computing is one way to achieve this.
Moreover, since AI computation requires processing a large amount of data, moving this data from cloud storage centers to high-end devices will add to the energy cost of any business.
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In contrast, the edge AI operational model eliminates this high cost in energy used to maintain AI operations in smart devices.
Responsiveness is one of the things that makes smart devices reliable and advanced AI ensures that. Edge AI solution increases the response rate of smart devices as there is no need to send data to the cloud for computation and then wait for processed data to be sent back for decision making.
Although the process of sending data to cloud-based data centers can be done within a few seconds, the advanced AI solution reduces the time it takes for smart devices to respond to requests by creating and processing data within the device.
With a high response rate, technologies such as self-driving vehicles, robots and other smart devices can provide instant feedback to both automatic and manual requests.
Edge AI use cases
Due to the increase in the use of AI to make IoT devices, software and hardware applications, the more advanced, smarter AI use cases have seen exponential growth. According to Allied Market Research, Global Edge AI devices market was valued at $6.88 billion in 2020, but is expected to reach $38.87 billion in 2030. From this figure, more high-end AI use cases are expected to emerge.
Meanwhile, some of the high-end AI use cases include facial recognition software, real-time traffic updates on self-driving vehicles, industrial IoT devices, healthcare, smart cameras, robots and drones. Additionally, video games, robotics, smart speakers, drones, and health monitoring devices are examples of where advanced AI is currently being used.