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Before we talk about "Edge Computing", what is "Peripheral Computing" or "Edge Computing"? Rather than the center, it is done closer to the source of the data, or "periphery". This approach aims to move computing functions from the data center to the source of the data to reduce latency, increase efficiency, and enable instant processing of data. For example, when using an Internet of Things (IoT - Internet of Things) device, the device itself may have processing capabilities so that the collected data can be analyzed immediately without having to first transmit it to a remote server. This is especially useful in applications that require fast response or local processing, such as self-driving cars or certain industrial automation scenarios.
AI Edge Computing, or "AI Edge Computing", is a technical strategy that optimizes the AI computing process and moves it to a device closer to the data source. Its purpose is to process data in real time, reduce latency, and improve computing efficiency. Compared with traditional cloud computing, edge computing allows data to be processed close to the point of generation, rather than relying on a remote central server. The advantage is that the data it collects can be analyzed and responded to immediately. Edge computing is particularly useful in IoT and AI applications because of its ability to provide low latency and high availability. There is no need to choose cloud computing and edge computing in cloud solutions, they do not "compete" with each other, they just complement each other and work together to provide better performance for applications.
AI An example of edge computing? Consider autonomous cars. EdgeArtificial intelligence allows vehicles to process data in real-time without relying on cloud servers. Why is this important? To ensure the safety and efficiency of your vehicle. Thisuse of edge AI for real-time coordination between vehicles is called Internet of Vehicles (IoV - Internet of Vehicles). In addition, intelligent traffic lights can adjust traffic flow in a timely manner to ensure road safety and efficiency. Also consider medical devices, edge AI allows medical devices to analyze patient data in real time without having to send it to the cloud. And in the energy sector, edge AI can interpret sensor data in real time, enhancing forecasting and decentralizing energy production.
However, even with AI computing at the edge so many advantages, there are still some challenges. For example, how to balance resource requirements and accuracy, data governance, protecting user privacy, and potential hardware requirements. However, as these technologies develop, they will indeed play a huge role in the future of multiple industries.
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