Edge AI refers to AI algorithms that are processed locally on hardware devices and can process data without a network connection. This means that operations such as data creation can be performed without streaming or data storage in the cloud. This is important because more and more device data cannot rely on cloud processing. For example, factory robots and self-driving cars need to process data at high speed with minimal delay.

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What is Edge AI?
The development of edge computing means that edge artificial intelligence is becoming more
and more important. All walks of life are better off, especially in terms of reducing processing
delays and protecting data privacy. This article will explore the importance of edge AI and its
common use cases.
Edge AI originated from edge computing. Edge computing, also known as edge processing, is a
network technology that places servers near local devices, which helps reduce the processing
load of the system and solve the problem of data transmission delays. Such processing is
performed near the sensor or where the device generates data, so it is called an edge.
The development of edge computing means that edge artificial intelligence is becoming more
and more important. All walks of life are better off, especially in terms of reducing processing
delays and protecting data privacy. This article will explore the impact of edge AI, why it is
important, and its common use cases.
What is edge AI?
Edge AI refers to AI algorithms that are processed locally on hardware devices and can process
data without a network connection. This means that operations such as data creation can be
performed without streaming or data storage in the cloud. This is important because more and
more device data cannot rely on cloud processing. For example, factory robots and self-driving
cars need to process data at high speed with minimal delay.
In order to achieve these goals, edge computing can rely on deep learning to generate data on
the cloud, and perform model inference and prediction at the origin of the data-the device itself

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(edge).
Take industrial robots in factories as an example. AI technology can visualize and evaluate a large
number of multi-modal data from surveillance cameras and sensors at a speed that humans
cannot reach, and can be used to detect fault data that humans may ignore on the production
line. This kind of IoT structure can store a large amount of data generated on the production line
and analyze it through machine learning. They are also the core of AI models that can increase
the intelligence of factories.
Edge AI, IoT and 5G
Edge AI is often discussed together with the Internet of Things (IoT) and 5G networks.
The term Internet of Things refers to devices connected to each other via the Internet, including
smartphones, robots, and electronic devices. As a platform that uses artificial intelligence for
analysis, edge artificial intelligence can collect and store a large amount of data generated by the
Internet of Things, making it possible to use a scalable cloud. This can increase the flexibility of
data processing and infrastructure.
5G network can enhance the above process, because its three major characteristics-ultra-high
speed, large concurrency, and ultra-low latency-are are significantly better than 4G network.
5G is indispensable to the development of the Internet of Things and edge AI, because when the
Internet of Things devices transmit data, the amount of data soars, which affects the
transmission speed. The decrease in transmission speed will cause time delay, and the time delay
is the biggest problem facing real-time processing.
Why are edge computing and edge AI important?
In more and more cases, device data cannot be processed through the cloud. This situation often
occurs in industrial robots and self-driving cars. They require high-speed processing, but when
the data flow increases and the processing delay occurs, it will be very dangerous.
For example, imagine a self-driving car detects objects on the road or operates the brakes or
steering wheel due to cloud delays. Any slowdown in data processing will result in slower
response speed of the vehicle. If the slower vehicle cannot respond in time, it may cause an
accident. Life will be truly threatened at this time.
For these IoT devices, real-time response is a necessary condition. This requires equipment to be
able to analyze and evaluate images/data on-site, instead of relying on cloud AI.
By handing the information processing normally entrusted to the cloud to the edge device,
real-time processing without transmission delay can be realized. In addition, if only important
information is transmitted to the cloud, the amount of data transmitted can be reduced, which
can minimize the risk of communication interruption.
Edge AI usage scenarios
The edge AI market has two main areas: industrial machinery and consumer equipment. It can be
seen that it has made progress in the fields of control and optimization of equipment,
automation and repetitive labor.
Consumer devices have also made breakthroughs. The AI cameras of these devices can
automatically identify the subject. Since the number of equipment is larger than that of industrial

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machines, it is expected that from 2021, the consumer equipment market will expand
substantially.
We put some common scenarios of edge AI below.
Self-driving car
Self-driving cars are the most anticipated field of edge computing. There are many situations in
which autonomous vehicles require a real-time assessment of the situation, which requires
real-time data processing. In December 2019, Japan revised the "Road Traffic Law" and the "Road
Transport Vehicle Law" to make it easier for Level 3 autonomous vehicles to go on the road.
It specifies the safety standards that self-driving cars should meet and the areas in which
self-driving cars can operate. Therefore, automakers are also working hard to develop
autonomous vehicles that comply with these standards. For example, Toyota is already testing
TRI-P4's fully automated (level 4) autonomous driving system.
Drone
There is more and more news about drones losing control and missing during flight. Some even
led to accidents. Depending on where the drone landed, the consequences of a crash can also be
catastrophic.
On an autonomous drone, the pilot does not actively interfere with the flight of the drone. They
monitor operations remotely, and only manually pilot drones when absolutely necessary. The
most famous example is Amazon's Prime Air, a drone delivery service that is developing
autonomous drones to deliver packages.
Face recognition
The face recognition system is the development direction of surveillance cameras, which can
recognize human individuals by learning faces. In November 2019, WDS Co., Ltd. released the AI
camera module Eeye to analyze facial features in real-time through edge AI. The Eeye can
recognize faces quickly and accurately and is suitable for marketing tools targeting gender, age,
and other characteristics, and face recognition scenarios used to unlock devices.
Smartphone
This is the edge AI device we are most familiar with. Siri and Google Assistant are good examples
of edge AI on smartphones because technology drives their voice UI. AI on mobile phones
enables data processing to occur on the device (edge) side, which means there is no need to
deliver device data to the cloud. This helps protect the privacy and reduce traffic.
Edge AI of the future
Edge AI is growing rapidly, and we have seen a lot of investment in this technology. Companies
like Konduit AI are using it as a key part of their AI strategy in Southeast Asia. Another example is
that in January 2020, Apple spent $200 million to acquire Xnor.ai, an AI company based in Seattle.
Xnor.ai's AI technology uses edge processing to process data on users' smartphones. As
smartphones have built-in artificial intelligence, we may see advances in voice processing, face
recognition technology, and privacy protection.
According to the 2019 AI business summary survey released by the Fuji Business Group, the
estimated market size of the edge AI computing market in Japan in the fiscal year 2018 is 11

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billion yen. The survey predicts that the market size will expand to 66.4 billion yen in fiscal 2030.
With the popularity of 5G, it may also see a decline in the cost of global edge AI services and an
increase in demand.