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

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

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

This article will gently explain the mechanisms and benefits of Edge AI so that even those hearing about it for the first time can understand.

What does 'Edge' in Edge AI mean?

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

• Edge … the "edge" of the network, meaning the field side

• Edge AI … technology that operates AI on the device side at the field instead of the cloud

Regular AI (Cloud AI) operates with the thought process of:

Send data to the cloud → AI computes in the cloud → Receive results back

This is a "cloud-centric" approach.

Edge AI, on the other hand, is the idea of not relying entirely on the cloud and making decisions as much as possible on the field-side devices.

• Smartphones

• Cameras

• Vehicle-mounted devices

• Machines on manufacturing lines

By putting AI models directly into the devices present in the field and making decisions on the spot, this is Edge AI.

Two Steps of AI Operation: 'Learning' and 'Inference'

AI primarily operates in two major steps.

1. Learning (Training)

2. Inference

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

In contrast, Edge AI has a role division where "learning" is done on the cloud side, and "inference" is primarily performed on the field devices.

Structure of Edge AI: Learning in the Cloud and Inferring in the Field

Following the image in the slides, let’s explain the flow of Edge AI a bit more concretely.

1. Training the model on the cloud

2. Compressing and lightweighting the model

3. Deploying (distributing) to edge devices

4. Conducting inference on-site

In this way,

"Learning on the cloud" → "Compress the model" → "Deploy to edge devices" → "Infer on-site"

is the fundamental structure of Edge AI.

Why Edge AI is Gaining Attention: Three Points

1. Real-time Performance (Almost No Latency)

The biggest advantage of Edge AI is 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 results".

In scenes where a moment's decision can be critical, this latency poses a significant risk.

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

2. Enhanced Security and Privacy

With Edge AI, there is the advantage of processing data without sending personal information to the cloud,

allowing for data to be processed within the device only.

• Facial data used for face recognition

• Detailed operational data of vehicles

• Data relating to healthcare and medicine

If sensitive information can be processed without being sent outside,

the risk of information leaks and unauthorized access can be reduced.

The approach of processing data as much as possible on-site without sending it outside will be increasingly 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 generates a massive amount of traffic.

• Surveillance cameras that operate 24/7

• Numerous sensors installed in factories, warehouses, and buildings

• Locations where communication environments are unstable, such as on ships, in mountains, and remote areas

In such environments,

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

• and notification occurs only when an anomaly is detected

, or only essential summary information is sent to the cloud

By designing the system this way,

it is possible to significantly reduce communication volume and communication costs.

Typical Scenarios Where Edge AI Excels

Edge AI is thriving in the following scenarios.

• Manufacturing sites

• Self-driving and in-vehicle systems

• Smart cities and infrastructure monitoring

• Locations with unstable communication

• At places where it is necessary to "make quick and intelligent decisions on-site without overly relying on the cloud", the value of Edge AI is expressed.

Conclusion: The ‘Best of Both Worlds’ of Cloud and Edge AI

To summarize the points:

• Edge AI is AI that conducts inference on field devices rather than the cloud

• The flow is

• The reasons for its increasing attention are

Future AI systems will use the approach of

learning from large data on the cloud, and making immediate decisions at the edge,

thus combining Cloud AI and Edge AI will become the mainstream.