Edge AI refers to the use of artificial intelligence (AI) technologies at the edge of a network, rather than in a centralized data center or the cloud. The “edge” refers to the physical location where data is generated and processed, such as a sensor on a factory floor or a camera in a traffic intersection.
One of the main benefits of edge AI is that it allows for real-time processing of data, as the data does not need to be transmitted to a centralized location for processing. This is particularly useful in situations where low latency, high throughput, or limited connectivity is required, such as in industrial automation, autonomous vehicles, and internet of things (IoT) applications.
Another benefit of edge AI is that it allows for more efficient use of resources, as the data does not need to be transmitted over a network to a centralized location for processing. This can also help to reduce the costs associated with transmitting and storing large amounts of data.
There are several types of edge AI technologies, including:
- Edge devices, such as sensors and cameras, that can perform basic AI processing on the data they generate.
- Edge gateways, which are devices that can aggregate data from multiple edge devices and perform more advanced AI processing on the data.
- Edge servers, which are small, low-power servers that can perform AI processing on data from edge devices and edge gateways.
The main drawback of Edge AI is that it requires specialized hardware and software that can support the processing of AI models on the edge. However, advancements in hardware and software technologies are making it possible to deploy AI models on a variety of edge devices, including low-power microcontrollers and gateways. With the growing popularity of IoT and Industry 4.0, it’s expected that Edge AI will become increasingly important in the coming years.
Overall, Edge AI is a way of using AI technologies to process data locally, at the edge of a network, rather than in a centralized data center or cloud. This can be useful in situations where low latency, high throughput, or limited connectivity is required, and it can also help to reduce the costs associated with transmitting and storing large amounts of data.
