What Is Edge AI Computing?
Edge AI starts with edge computing. Also called edge processing, edge computing is a network technology that positions servers locally near devices. This helps to reduce system processing load and resolve data transmission delays. These processes are performed at the location where the sensor or device generates the data, also called the edge.
Developments in edge computing mean that edge AI is becoming more important. This is true across a variety of industries, particularly when it comes to processing latency and data privacy. In this article we’ll look at the impact of Edge AI, why it’s important, and common use cases for it.
Edge AI refers to AI algorithms that process locally on hardware devices, and can process data without a connection. This means operations such as data creation can occur without streaming or storing data in the cloud. This is important because there are an increasing number of cases where device data can’t be handled via the cloud. Factory robots and cars, for example, need high-speed processing with minimal latency.
To achieve these goals, edge computing can generate data through deep learning on the cloud to develop deductive and predictive models at the data origin point, i.e. the device itself (the edge).
We can see an example of this at work in factory robots. AI technology can be used here to visualize and assess vast amounts of multimodal data from surveillance cameras and sensors at speeds humans can’t process. We can also use it to detect faulty data on production lines that humans might miss. These kinds of IoT structures can store vast amounts of data generated from production lines and carry out analysis with machine learning. They are also at the heart of the deductive and predictive models that improve the smartification of factories.
Edge AI is often talked about in relation to the Internet of Things (IoT) and 5G networks. The term IoT refers to devices connected to each other through the internet, and includes smartphones, robotics, and electronic devices. As a platform that performs analysis with AI, edge AI can collect and store the vast amount of data generated by IoT, making it possible to use clouds with scalable characteristics. This allows for improved data processing and infrastructural flexibility.
5G networks can enhance the above-mentioned processes because their three major features — ultra-high speed, massive simultaneous connections, and ultra-low latency — clearly surpass that of 4G. 5G is indispensable for the development of IoT and edge AI, because when IoT devices transmit data, data volume swells and impacts transfer speed. Drops in transfer speed can create latency, which is the biggest issue when it comes to real-time processing.
There are an increasing number of cases in which device data can’t be handled via the cloud. This is often the case with factory robots and cars, which require high-speed processing because of issues that can arise when increased data flow creates latency.
For example, imagine a self-driving car suffering from cloud latency while detecting objects on the road, or operating the brakes or steering wheel. Any slowdown in data processing will result in a slower response from the vehicle. If the slowdown is such that the vehicle does not respond in time, this could result in an accident. Lives are literally at risk.
For these IoT devices, a real-time response is a necessity.
