The advantages of Edge AI

Edge AI is a new computing paradigm that incorporates AI into edge computing frameworks. Here are some of the benefits and use cases.

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Adoption of edge computing has seen significant growth in recent years. A recent report from Research and Markets notes that the size of 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 rise of IoT applications and enterprise data, there is a growing demand to develop devices that can handle information processing faster and smarter. This is where AI edge comes to life.

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The integration of artificial intelligence into edge computing or artificial intelligence has enabled peripheral devices to use artificial intelligence algorithms to process information at the edge of the device or on a server near the device, reducing the time it takes for peripheral devices to make IT decisions.

What is on-board AI?

The concept of edge AI involves applying AI to edge computing. Edge computing is a computing paradigm that allows data to be generated and processed at the edge of the network rather than in a central data center. Therefore, artificial intelligence is integrating AI into edge computing devices for faster and improved data processing and intelligent automation.

Advantages of edge AI

Data security and privacy

With the increasing number of data beams recorded in recent years, many companies are looking for other ways to improve data privacy. Edge AI provides an enabling ground for data privacy because data processing activities are performed on the edge of the device or closer to the device. As a result, the number of data sent to the cloud for computation is drastically reduced. Furthermore, when data is created 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 analysis

Real-time data processing has become critical due to the explosive growth of data generated by mobile devices and IoT at the edge of the network. Therefore, one of the main 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 perimeter AI, the data needed to apply the AI ​​to the perimeter devices is stored on the device or on a nearby server rather than in the cloud. This form of calculation reduces latency in the calculation and quickly returns the processed information.

Low Internet Bandwidth

The growing amount of data generated by billions of devices around the world results in an explosive need for internet bandwidth to process data from cloud storage centers. This practice forces companies to commit huge amounts of money to bandwidth purchases and subscriptions.

However, with perimeter AI, there is a significant reduction in the volume of bandwidth required to process information at the edge. Also, because the perimeter AI calculates and processes data locally, less data is sent to the cloud over the internet, thus saving an enormous amount of bandwidth.

Lower energy consumption

Maintaining a back and forth connection with cloud data centers consumes a lot of energy. As a result, many companies are looking for ways to reduce their energy bills, and edge computing is one of the ways to achieve this.

Additionally, since AI computation requires processing a large amount of data, transporting this data from cloud storage centers to edge devices will increase any company’s energy cost.

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In contrast, the edge AI operating model eliminates this high cost in the energy used to maintain AI processes in smart devices.

Better responsiveness

Responsiveness is one of the things that makes smart devices reliable, and edge AI ensures that. An AI edge 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 the processed data to be sent back for decision making.

While the process of sending data to cloud-based data centers can be done in seconds, the edge AI solution further reduces the time it takes for smart devices to respond to requests by generating and processing data within the device.

With a high response rate, technologies such as autonomous vehicles, robots and other smart devices can provide instant feedback to automatic and manual requests.

Edge AI use cases

Due to the increase in the use of AI to build IoT devices, software and hardware applications, smarter, perimeter AI use cases have witnessed tremendous growth. According to Allied Market Research, the global Edge AI hardware market was valued at $ 6.88 billion in 2020, but is expected to reach $ 38.87 billion in 2030. From this number, more cases are expected to emerge. use of the AI ​​edge.

Meanwhile, some use cases for edge AI include facial recognition software, real-time traffic updates on autonomous vehicles, industrial IoT devices, healthcare, smart cameras, robots, and drones. Additionally, video games, robots, smart speakers, drones, and health monitoring devices are examples of where edge AI is currently being used.

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