Edge AI is the use of artificial intelligence directly on or near the device where data is generated, rather than sending all data to a distant cloud server for processing.
For example, a security camera using Edge AI can analyze video on the camera or a nearby computing device and detect a person without continuously uploading the entire video stream to the cloud.
Edge AI combines AI models with edge computing. It is used when fast responses, reduced network usage, privacy, or the ability to operate with limited internet connectivity is important.
What Is Edge AI?
Edge AI refers to running AI models on edge devices, such as smartphones, cameras, sensors, vehicles, industrial equipment, gateways, or other computing hardware located close to the source of the data.
Traditional cloud-based AI typically sends data over the internet to servers for processing. With Edge AI, some or all of that processing happens locally.
For instance, a smartphone can use an AI model on the device to recognize a face in a photo. The image does not necessarily need to be uploaded to a remote server for the recognition task to occur.
Edge AI is not a separate type of artificial intelligence. It describes where AI processing takes place.
How Does Edge AI Work?
An AI model is usually trained using computing infrastructure with sufficient processing power. Once prepared and optimized, the model can be deployed to an edge device.
The basic process looks like:
Device collects data → AI model processes data locally → Model produces result → Device takes or recommends an action
Consider a factory camera inspecting products on a production line. The camera captures an image, an AI model analyzes it for defects, and the system can flag a problem without first sending every image to a cloud data center.
Some Edge AI systems still communicate with cloud services for model updates, data storage, additional analysis, or tasks that require more computing power. Edge and cloud AI can therefore work together rather than being mutually exclusive.
Edge AI vs Cloud AI
The main difference between Edge AI and cloud AI is where the AI model processes data.
With Edge AI, processing occurs on a device or nearby computing system. This can reduce the time needed to send data elsewhere and receive a response.
With cloud AI, information is sent to remote servers where models perform the required processing. Cloud infrastructure can provide substantially more computing resources and may be better suited to large or computationally demanding models.
Many applications use a combination of both approaches. Local processing can handle immediate tasks while cloud systems perform more resource-intensive operations.
What Are Examples of Edge AI?
Smartphones can use on-device AI for photography, speech processing, biometric features, and other functions.
Security cameras can analyze video locally to identify objects, movement, or specific events.
Vehicles can process information from cameras and sensors close to where the data is generated, which is important for functions requiring rapid responses.
Industrial equipment can use AI to identify defects, monitor machinery, or detect unusual operating conditions.
Smart home devices may process voice, images, or sensor information locally instead of sending every piece of raw data to external servers.
Benefits of Edge AI
One major benefit of Edge AI is lower latency. Processing data close to its source can reduce the delay caused by sending information to and from remote servers.
Edge AI can also reduce bandwidth usage because devices do not always need to transmit large volumes of raw data.
Privacy can be another benefit when sensitive information is processed locally and does not need to leave the device. However, local processing alone does not automatically make a system private or secure.
Edge AI can also help applications continue functioning when an internet connection is slow, unreliable, or unavailable.
Limitations of Edge AI
Edge devices generally have less computing power, memory, storage, and energy available than cloud infrastructure. AI models may therefore need to be smaller or optimized before they can run efficiently on these devices.
Managing models across many devices can also be difficult. Developers may need systems for distributing updates, monitoring performance, protecting devices, and maintaining model versions.
The choice between edge and cloud processing ultimately depends on factors such as latency, hardware capabilities, privacy requirements, cost, connectivity, and the complexity of the AI model.
