What is Edge AI? A Beginner's Guide to Understand the Differences with the Cloud in 3 Minutes

Recently, the term "Edge AI" has been frequently heard.

However, I think many of you may be wondering, "What is the difference from Cloud AI?" or "In what scenarios is it used?"

In this article, I will gently explain the mechanism and benefits of Edge AI in a way that even those who are hearing about it for the first time can understand.

What does 'Edge' in Edge AI mean?

First, let's clarify the meaning of the terms.

• Edge … the "edge" of the network, that is, the site side

• Edge AI … technology that runs AI on the device side at the site rather than in the cloud

Ordinary AI (Cloud AI) operates on a

Send data to the cloud → AI calculates in the cloud → Result is returned

approach that is "cloud-centric".

Edge AI, on the other hand, is the approach to not rely entirely on the cloud and make decisions as much as possible on devices at the site.

• Smartphones

• Cameras

• In-vehicle devices

• Machines on manufacturing lines

This means implementing AI models into terminals at the site and allowing them to make decisions right there, which is Edge AI.

Two Steps Where AI Operates: 'Learning' and 'Inference'

AI generally consists of two main steps.

1. Learning (Training)

2. Inference

Cloud AI typically performs both learning and inference on the cloud side.

On the other hand, Edge AI distributes the roles where "Learning" is done on the cloud and "Inference" is primarily performed on site devices.

Structure of Edge AI: Learning in the Cloud, Inferring on Site

Following the image in the slide, let me explain the flow of Edge AI in a bit more detail.

1. Train the model in the cloud (learning)

2. Compress and lighten the model

3. Deploy (distribute) to edge devices

4. Perform inference on site

In this way,

"Learning in the cloud" → "Compressing the model" → "Deploying to edge devices" → "Inferring on site"

is the fundamental structure of Edge AI.

Why is Edge AI attracting attention: 3 points

1. Real-time capability (almost no latency)

The greatest benefit of Edge AI is its immediacy.

• Self-driving cars

• Manufacturing sites

If data is sent to the cloud every time,

there will inevitably be a time lag of "Send → Process → Receive result".

In situations where instant decisions are critical, this latency poses a significant risk.

With Edge AI, processing can be completed within the device, minimizing latency.

2. Improved security and privacy

In Edge AI, data that includes personal information does not need to be sent to the cloud,

allowing for processing solely within the device.

• Facial data used for face recognition

• Detailed operation data of vehicles

• Data related to medical and healthcare

By processing such sensitive information without exposing it externally, the risks of data leaks and unauthorized access can be reduced.

The idea of "processing as much data as possible on-site without sending it out" is becoming an increasingly important area.

3. Reduction of communication costs

In areas where a large number of surveillance cameras and IoT devices are installed, continuously sending all data to the cloud results in massive communication volume.

• Surveillance cameras operating 24/7

• Numerous sensors installed in factories, warehouses, and buildings

• Locations with unstable communication environments such as on ships, in the mountains, or isolated areas

In such environments, raw data from video or sensors is processed on the edge, and notifications are only sent when there are abnormalities, or only the necessary summary information is sent to the cloud, which can significantly reduce communication volume and costs.

Typical Scenes Where Edge AI Excels

Edge AI is active in the following situations.

• Manufacturing Sites

• Self-Driving and In-Vehicle Systems

• Smart Cities and Infrastructure Monitoring

• Sites with Unstable Communication

• In places where it is required to "make quick and smart decisions on-site without over-relying on the cloud," the value of Edge AI is demonstrated.

Conclusion: The Future of AI is Combining the Best of Cloud and Edge

To summarize the points:

• Edge AI is an AI that performs inference on devices at the site rather than in the cloud.

• The flow is 【Learning in the Cloud → Lightening the Model → Deploying to Edge Devices → Inferring on Site】.

• Reasons for its attention are

The future AI systems will combine cloud-based learning for large data, and edge-based immediate decision-making, and this flow is likely to become mainstream.