Edge computing adoption has witnessed a significant amount of growth in recent years. A recent report by Research and Markets records that the global edge computing market size is anticipated 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 in IoT applications and business data, there is a growing demand to develop devices that can handle information processing faster and smarter. This is where edge AI comes to life.
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The integration of AI into edge computing or edge AI has made it possible for edge devices to utilize AI algorithms to process information at the edge of the device or on a server near the device, cutting down the time it takes edge devices to make computing 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 network edge rather than at a central data center. Therefore, edge AI is integrating AI into edge computing devices for quicker and improved data processing and smart automation.
Benefits of edge AI
Data security and privacy
With the growing number of data reaches recorded in recent years, many businesses are looking for more ways to improve data privacy. Edge AI provides an enabling ground for data privacy because data processing activities are performed at the edge of the device or closer to the device. As a result, the number of data sent to the cloud for computation has drastically reduced. In addition, when data is created and processed at the same location, it increases data security and privacy, making it more difficult for hackers to get onto your data.
Processing data in real-time has become vital due to the explosive growth of data generated by mobile and IoT devices at the network edge. Hence, 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 edge AI, the data needed to apply AI in edge devices are stored in the device or a nearby server rather than in the cloud. This form of computing reduces latency in computation and returns processed information quickly.
Lower internet bandwidth
The growing amount of data generated from billions of devices across the globe results in an explosive need for internet bandwidth to process data from cloud storage centers. This practice forces businesses to commit a huge amount of money to bandwidth purchases and subscriptions.
However, with edge AI, there is a significant reduction in the volume of bandwidth required to process information at the edge. In addition, since edge AI computes and processes data locally, fewer data are sent to the cloud through the internet, thereby saving a huge amount of bandwidth.
Lesser power consumption
Maintaining a back-and-forth connection with cloud data centers consumes a lot of energy. As a result, many businesses are looking for ways to cut down on energy bills, and edge computing is one of the ways to achieve this.
Furthermore, because AI computation requires processing a high amount of data, transporting this data from cloud storage centers to edge devices will add to the energy cost of any business.
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In contrast, the operational model of edge AI eliminates this high cost in the energy used to maintain the AI processes in smart devices.
Responsiveness is one of the things that makes smart devices reliable and edge AI guarantees that. An 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 the 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 edge AI solution further reduces the amount of time it takes smart devices to respond to requests by generating and processing the data within the device.
With a high response rate, technologies like autonomous vehicles, robots and other intelligent devices can provide instant feedback to 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, more intelligent, edge 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 projected to hit $38.87 billion in 2030. From this number, more edge AI use cases are expected to emerge.
Meanwhile, some edge AI use cases include facial recognition software, real-time traffic updates on autonomous vehicles, industrial IoT devices, health care, smart cameras, robots and drones. Additionally, video games, robots, smart speakers, drones and health monitoring devices are examples of where edge AI is currently used.
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