Edge AI is a new computing paradigm that integrates AI into edge computing frameworks. Here are some of the benefits and use cases.
Edge computing adoption has seen significant growth in recent years. According to a recent report by Research and Markets, 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 the industry is artificial intelligence. With the growth of IoT applications and business data, there is a growing demand to develop devices that can handle information processing faster and smarter. This is where the Edge AI comes to life.
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The integration of AI into Edge Computing or Edge AI has enabled edge devices to use AI algorithms to process information at the device edge or on a server near the device, reducing the time it takes edge Devices need to make computational decisions.
What is Edge AI?
The concept of edge AI implies the application of 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, Edge AI integrates AI into edge computing devices for faster and improved data processing and intelligent automation.
Benefits of Edge AI
Data security and data protection
With the growing number of data breaches recorded in recent years, many organizations are looking for additional ways to improve data protection. Edge AI provides a foundation for privacy as data processing activities are performed at the edge of the device or closer to the device. As a result, the amount of data sent to the cloud for computation has drastically reduced. In addition, having data created and processed in the same place increases data security and privacy, making it harder for hackers to get to your data.
Processing data in real-time has become critical due to the explosive growth of data generated by mobile and IoT devices at the network edge. Therefore, one of the main benefits of Edge AI is that it facilitates real-time data processing by ensuring powerful data computation on IoT devices.
This is possible because with edge AI, the data needed to apply AI to edge devices is stored on the device or a nearby server, not in the cloud. This form of data processing reduces computational latency and quickly returns processed information.
Lower internet bandwidth
The growing amount of data generated by billions of devices around the world is driving an explosive demand for internet bandwidth to process data from cloud storage centers. This practice forces companies to allocate a huge amount of money for bandwidth purchases and subscriptions.
However, with edge AI, the amount of bandwidth required to process information at the edge is significantly reduced. Also, because the edge AI computes and processes data locally, less data is sent over the internet to the cloud, saving huge amounts of bandwidth.
Low energy consumption
Maintaining a back-and-forth connection to cloud data centers consumes a lot of energy. As a result, many companies are looking for ways to reduce their energy costs, and edge computing is one of the ways to achieve this.
Furthermore, since AI computation requires the processing of a large amount of data, transporting this data from cloud storage centers to edge devices increases the energy costs of every company.
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In contrast, the Edge AI operating model eliminates this high cost of energy used to sustain the AI processes in smart devices.
Responsiveness is one of the things that makes smart devices reliable, and Edge AI guarantees it. An edge AI solution increases the response rate of smart devices by eliminating the need to send data to the cloud for computation and then wait for the processed data to be sent back for decision making.
Although sending data to cloud-based data centers can be done in a matter of seconds, the Edge AI solution further reduces the time it takes smart devices to respond to requests by generating and processing the data within the device.
With a high response rate, technologies such as autonomous vehicles, robots, and other smart devices can provide instant feedback on automatic and manual requests.
Edge AI Use Cases
Smarter edge AI use cases have seen tremendous growth due to the increasing use of AI to manufacture IoT devices, software and hardware applications. According to Allied Market Research, the global edge AI hardware market was valued at US$6.88 billion in 2020 but is projected to reach US$38.87 billion in 2030. More edge AI use cases are expected to emerge from this number.
Meanwhile, some edge AI use cases include facial recognition software, real-time traffic updates for 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 deployed.