Explanation of the difference between Edge AI and Cloud AI for beginners

Contact

What is the difference between Edge AI and Cloud AI?

Cloud AI sends data to data centers via the Internet and processes it on a distant server. Large-scale AI like ChatGPT and image generation AI is close to this image.

Edge AI processes AI in a location close to terminals and sites such as smartphones, cameras, automobiles, and factory facilities. It is used for face authentication, photo correction, autonomous driving support, surveillance camera analysis, etc.

ざっくり言えば、

Cloud AI is a system that borrows a large brain.
Edge AI has a small brain in the terminal.

With this difference, you can easily see the connections between AI semiconductors, data centers, smartphones, robots and automobiles.

Cloud AI

Cloud AI is a system that sends data to data centers via the Internet and processes AI on the server side.

The image is as follows:

Smartphone / PC / terminal
  ↓
Internet
  ↓
DataCenter
  ↓
AI Processing
  ↓
Returns results to the terminal

Typical examples include:

AI services such as ChatGPT
Image Generation AI
Large voice recognition service
Data analysis AI for companies
AI model running on the cloud

Cloud AI’s strength is the size of computing power. We use huge GPU servers and data centers to help you learn large-scale models, analyze large-scale data, and make complex論s.

Instead, communication is required. The network is slow, mixed, and not connected. In such a situation, the reaction may be delayed or not processed.

Edge AI

Edge AI is a mechanism to move AI in the device or in the field.

Smartphone/Camera/Car/Factory Equipment
  ↓
AI chip and AI model in the terminal
  ↓
On the spot

In the familiar example, there is face authentication and photo correction of smartphone. It is not to send the information taken by the camera to the cloud every time, but to process it with chips and AI models in the terminal.

Industrial applications are used in the following situations:

Driving support for cars
Monitoring camera person detection
Factory abnormality detection
Robots
Sensor analysis of stores and warehouses
物 Obst Detection

Edge AI's strength is the speed of reaction. Because there is no time to wait for time to send to the cloud, it is easy to get low latency. Even if communication is unstable, it is also great to continue processing.

On the other hand, there are limitations on terminal computing power, power, fever and cost. It is difficult to do the same thing as the huge GPU server in the cloud as it is in the smartphone or camera.

Comparison Chart: Difference between Edge AI and Cloud AI

項目	Edge AI	Cloud AI
Processing place	On-site, car, camera, factory, etc.	DataCenter
Contact	may be left unnecessary or less	Basically necessary
Speed	Low latency	Depends on the communication environment
Company Profile	Im ate judgment, field control, lightweight reasoning	Large-scale learning, large-scale analysis, high-performance reasoning
Privacy Policy	Data may be removed	Design of data transmission and storage
weakness	Constraints for terminal performance, power and heat generation	Communication costs, delays, data center costs

For beginners, it is easy to understand by thinking "where the process is reasonable" rather than "w。 is superior".

Figure: Process flow of cloud AI and edge AI

Why Edge AI?

The more AI usage, the more cloud-based design.

Especially video, audio and sensor data are large. If you continue to send all the video of the surveillance camera to the cloud, the communication costs and server costs will be increased. If you need to judge milliseconds like a factory or a car, it is risk to wait for the communication round-trip.

For example, in the car, it is a problem to detect pedestrians and obstacles and delay to brake judgment.ですs and robots are the same. In applications that require instant judgment in the field, there are scenes that do not fit in the process of listening to the cloud.

Therefore, the design is increasing by determining the terminal and sending only the necessary information to the cloud.

Cloud AI and Edge AI are not

As Edge AI expands, it does not require cloud AI.

The ability to compute clouds and data centers is essential to learning a large AI model. We are good at cloud processing that collects and analyzes huge amounts of data.

On the other hand, it is better to move lightly and faster on the device side in the actual scene.

よく言われるのが、

Learning Cloud, Run Edge

It is a way of thinking. Of course, it is not all of this form, but it is quite easy to understand as the direction.

Create a large model in the cloud and move a small model according to the application on the edge side. When this increases, the AI market is not just about data centers, but also the story of terminal semiconductors and device design.

Investors

If you look at it as an investment theme, Edge AI and Cloud AI are not seen separately, but it is closer to the actual situation.

In the cloud AI side, data center, GPU, network, power and cloud services are important. Microsoft, Amazon, Google, NVIDIA, AMD, etc.

Edge AI focuses on AI semiconductors, NPUs,カメラ, cameras, in-vehicle chips, and low power consumption design. Qualcomm、Apple、Samsung Electronics、Automo。 semiconductor manufacturers、Industrial equipment manufacturers

分野ing the fields to be seen is as follows:

分野	注目点
AI Semiconductor	GPU, NPU, AI Accelerator, Automotive Chip
Cloud	Data center, GPU server, AI service
Home	Smartphone, PC, Car, Camera, IoT device
Contact	5G, Wi-Fi, Low Latency Network
Industrial Equipment	Factory Automation, Robot, Inspection Equipment

However, it doesn’t grow anything if you add AI. Edge AI provides not only performance but also power consumption, unit price, mass production, and software support. In cloud AI, even if the demand for calculation increases, power costs and capital investment burden may be increased.

It is better to see a little。. The center of the AI boom looks like a生成 generation AI, but the difference in the geographic design that “where to calculate” is effective in the cost structure and profitability of the company.

How to learn for beginners

It is easy to understand by cooking.

Cloud AI is an image that sends ingredients to restaurants and cook them in a professional kitchen. High quality and large processing, but it takes time to move.

Sending ingredients to restaurants
  ↓
Cooking in professional kitchen
  ↓
Get back

Edge AI is an image of cooking in your kitchen. There are limitations on equipment and materials.

Cooking at home kitchen
  ↓
You can eat immediately

In other words, Cloud AI is strong in processing, and Edge AI is strong in the field judgment. By separating this article, it will be quite easy to organize when words such as "AI terminal", "AI PC", "In-vehicle AI", and "Data center investment".

What is the difference between Edge AI and Cloud AI?

Cloud AI performs high-performance large-scale processing in data centers
Edge AI performs high-speed and low-latency processing on terminals and sites
Cloud AI is suitable for large-scale learning and large-scale analysis
Edge AI is ideal for immediate decisions such as cars, cameras, factories, and smartphones.
In the future, it is easier to design combinations of cloud and edges

In the case of investing, you can see only AI software to see the overall picture.

Semiconductor, data center, communication, terminal, sensor, industrial equipment. The AI market is expanding together with the surrounding industry.

If you press the flow of "学習ing is cloud, execution is edge", you can see AI-related news.

Reference

IBM, What Is Edge AI?
IBM, Edge AI vs. Cloud AI
NVIDIA Blog, What Is Edge AI and How Does It Work?
Intel, Edge AI
Red Hat, What is edge AI?

This article is for educational and informational purposes only, based on public information. It is not a recommendation or solicitation to buy or sell any specific security or financial product. Although care is taken with accuracy, the content and future investment outcomes are not guaranteed. Final investment decisions should be made at your own judgment and responsibility.