What is Edge AI? An Introductory Guide to Understand the Differences from Cloud in 3 Minutes

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

However, many might wonder, "What is the difference from Cloud AI?" and "In what situations is it used?"

This article explains the mechanisms and benefits in a way that even those hearing about Edge AI for the first time can understand.

What does “Edge” in Edge AI mean?

First, let’s clarify the meaning of the term.

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

• Edge AI … technology that runs AI on devices on-site rather than in the cloud

Normal AI (Cloud AI) operates on the following principle:

Send data to the cloud → AI computes in the cloud → Results are returned

This is a "cloud-centered" approach.

Edge AI is an approach that does not rely entirely on the cloud, but makes as many judgments as possible on-site using local devices.

• Smartphones

• Cameras

• In-vehicle devices

• Machines on the manufacturing line

Edge AI involves embedding AI models into devices on-site to make decisions right there.

Two Steps for AI Operation: “Learning” and “Inference”

AI generally has two main steps.

1. Learning (Training)

2. Inference

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

In contrast, Edge AI separates roles by conducting learning on the cloud side and performing inference mainly on-site using local devices.

Structure of Edge AI: Learning in the Cloud, Inference on-site

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

1. Training the model on the cloud

2. Compressing and optimizing the model

3. Deploying the model to edge devices

4. Performing inference on-site

Thus,

“Learning on the cloud” → “Compressing the model” → “Deploying to edge devices” → “Inference on-site”

is the basic structure of Edge AI.

Why Edge AI is Gaining Attention: Three Points

1. Real-Time Performance (Almost No Delay)

The greatest advantage of Edge AI is its immediacy.

• Self-driving cars

• Manufacturing sites

If data is sent to the cloud every time,

a time lag of "Send → Process → Receive results" inevitably occurs.

In situations where a split-second decision can be critical, this delay poses a significant risk.

Edge AI minimizes delay because it can complete processes within the device itself.

2. Enhanced Security and Privacy

With Edge AI, it is advantageous because data containing personal information is not sent to the cloud.

It can be processed entirely within the device.

• Facial data used for face recognition

• Detailed operational data of vehicles

• Healthcare and medical data

If such sensitive information can be processed without being sent outside,

the risks of information leakage and unauthorized access can be reduced.

The idea of "processing as much data as possible on-site without sending it out"

will become even more important in the future.

3. Reduction of Communication Costs

In environments where numerous 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 such as on boats, in mountains, or rural areas with unstable communication

In such settings,

• The raw data from videos and sensors can be processed on the edge,

• and only notify when an anomaly occurs or send necessary summarized information to the cloud

This configuration can significantly reduce

communication volume and costs.

Typical Scenes Where Edge AI Excels

Edge AI is active in the following scenarios.

• Manufacturing sites

• Autonomous driving and in-vehicle systems

• Smart cities and infrastructure monitoring

• Areas with unstable communication

• In places where it is essential to "make quick and intelligent decisions without over-relying on the cloud", the value of Edge AI shines.

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

In summary:

• Edge AI is AI that performs inference on-site rather than in the cloud

• The flow is

• The reasons for gaining attention are

Future AI systems will implement

large data training on the cloud while making instant decisions on the edge,

leading to a trend of combining Cloud AI and Edge AI.