Introduction to Edge AI Technology
— AI That Thinks and Acts at the Edge —
■Where AI Runs Determines Its Value
Artificial intelligence (AI) technologies have advanced rapidly, largely driven by the use of large-scale computing resources in the cloud. However, in real-world image sensing environments, challenges unique to on-site operations still remain—such as network stability, communication latency, operational costs, and the need to protect privacy.
Against this backdrop, Edge AI, in which AI processing is performed directly on devices at the edge, has gained increasing attention. At i-PRO, Edge AI functions are integrated into nearly all of our security camera products, and Edge AI is positioned as a core pillar of our product development strategy.
Why does i-PRO promote Edge AI?
The answer lies in a fundamental question: where should AI run? We believe that this decision has a significant impact on real-world value. Running AI at the edge is, in many cases, the most practical and rational choice for our customers.
In this article, we compare Edge AI and Cloud AI, outline their respective strengths and challenges, and explain the direction i-PRO is pursuing with Edge AI.
■Cloud AI and Edge AI: Strengths and Challenges
Today, AI is most commonly deployed in the cloud. Cloud AI collects data from devices and processes it using powerful centralized computing resources. This approach enables highly advanced and accurate analysis and is one of its greatest strengths.
However, Cloud AI also presents several challenges:
- ・ Latency caused by transmitting large volumes of data over networks
- ・ Operational disruptions when network connectivity is lost
- ・ Increased operational costs, including communication fees and cloud usage charges
In contrast, Edge AI embeds AI capabilities directly into devices such as cameras and sensors, performing AI processing locally on the device. Only the analysis results are transmitted to the cloud, significantly reducing the volume of data sent.
Key advantages of Edge AI include:
- ・ Near-zero latency, enabling real-time decision-making
- ・ Operation independent of network connectivity, including offline environments
- ・ Reduced communication and operational costs
- ・ Enhanced privacy protection by keeping video data on the device
On the other hand, Edge AI devices have limited computing resources. As a result, AI accuracy may be lower than that of Cloud AI, and updating AI models across multiple devices can require additional effort.
It should be noted that some architectures referred to as Edge AI use edge servers positioned between devices and the cloud to perform AI processing. However, to fully leverage the inherent benefits of Edge AI—such as cost efficiency and real-time responsiveness—i-PRO adopts an approach in which AI is embedded directly within edge devices (cameras) themselves.
■Selecting the Right AI Architecture for Each Use Case
Both Cloud AI and Edge AI have clear strengths and are suited to different applications.
Cloud AI is well suited for areas that require large-scale data analysis and long-term forecasting, such as marketing analytics.
By contrast, Edge AI excels in environments where real-time processing and immediate on-site decision-making are essential, including:
- ・ Manufacturing lines
- ・ Traffic control systems
- ・ Autonomous driving
- ・ Various IoT applications
Many of i-PRO’s customers operate in mission-critical environments such as airports, public facilities, factories, and medical institutions. In these settings, instantaneous and accurate decisions are essential, along with stable operation and optimized costs.Taking these requirements into account, i-PRO places Edge AI at the center of its technology development. While Edge AI presents certain challenges, we continue to invest in research and development to overcome them.
■i-PRO’s Approach: Addressing the Challenges of Edge AI
[Improving AI Accuracy Through Continuous Learning (MLaaS)]
Because Edge AI operates under limited computing resources, maintaining high AI accuracy can be challenging. To address this issue, i-PRO has developed a continuous learning framework based on MLaaS (Machine Learning as a Service).
An “AI trainer” deployed in the cloud evaluates detection results generated by Edge AI devices in the field and continuously retrains and refines the models. Through this process, i-PRO aims to achieve AI accuracy comparable to that of Cloud AI—while keeping costs under control.
[Mitigating Physical Security Risks]
In environments such as outdoor installations, devices may be physically accessible, increasing the risk of tampering or unauthorized access. To mitigate these risks, i-PRO encrypts all communications and data and applies digital signatures to establish an end-to-end secure environment.
These measures ensure the authenticity and integrity of video data and AI models.
[Reducing Initial Deployment Costs Through AI Enablement of Existing Systems]
Deploying AI-enabled cameras typically requires an initial investment. To reduce this burden, i-PRO provides a solution in which a single AI-enabled camera can analyze video streams from existing non-AI cameras.
This approach allows customers to leverage existing assets while introducing AI in a phased manner, minimizing upfront costs and enabling gradual deployment.
We have also been working for many years on the challenge of reducing power consumption. When power consumption increases, heat is generated inside small devices like cameras, and that heat further reduces power efficiency, creating a vicious cycle. To break this cycle, we focus on using the latest chips that contribute most to lowering power consumption, and on preventing heat generation as well as efficiently dissipating any heat that does occur.
■Ethics and Trust: Responsible Use of AI
As AI becomes an integral part of social infrastructure, companies are expected to uphold high standards of transparency, fairness, and privacy protection. At i-PRO, the ethical and responsible use of AI is a top priority.
We have established our own AI Ethics Principles and formed an AI Ethics Committee to ensure robust AI governance and appropriate operational management. In May 2025, i-PRO became the first company in the security industry to obtain certification under ISO/IEC 42001, the international standard for AI management systems.
As a leading company in Edge AI technology, i-PRO is committed to implementing AI ethically and responsibly across all product lines. Building on our accumulated AI expertise, we will continue to expand into public safety, healthcare, industrial applications, and future markets—striving to remain a trusted partner to our customers and to society through sincere and continuous technological innovation.