What is Edge AI Enabling in Industrial Computing Applications?

Publish Date:
January 15, 2024

Similar to how cloud computing evolved over the last decade to the de facto way of storing and managing data, Edge AI is taking off. Edge AI is one of the most notable trends in artificial intelligence, as it allows people to run AI processes without having to be concerned about security or slowdowns due to data transmission. And its impact is notable in industrial embedded computing, since it allows platforms to react quickly to inputs without access to the cloud.

We asked some Edge AI partners: If analytics can be performed in the cloud, what is the benefit of an Edge AI approach, especially as it’s related to industrial embedded computing?

Andreas Lambauer, General Manager Europe, Hailo Technologies LTD.

Cloud computing offers many benefits for video analytics, however with industrial embedded computing applications, there are multiple advantages to an Edge AI approach, especially when performing video analytics on the edge.

The main benefit of an Edge AI approach is reduced latency. In many industrial applications, such as optical inspection for anomaly detection or product identification, it’s critical to make real-time decisions based on sensor data.  The time it takes to transmit that data to the cloud and receive a response can be too slow, resulting in slower operation and therefore lower productivity.

Performing AI analytics on the edge means data processing can be significantly faster. This enables real-time decision-making and response, removes the analytics bottlenecks and allows machines to run at full speed.

Another advantage of edge AI is improved reliability and security. Industrial environments can be harsh and unpredictable, and devices may need to operate in conditions where internet connectivity is unreliable or unavailable. With an Edge AI approach, computers can continue to operate independently, even when internet connectivity is lost.

AI computing helps identify hazards and security breaches and alerts employees and manufacturing managers in real-time, preventing injuries and unnecessary damage to equipment or personnel. Additionally, because data does not need to be transmitted to the cloud for analysis, there is less risk of sensitive data being intercepted or compromised during transmission.

Furthermore, an Edge AI approach can significantly reduce bandwidth and storage costs by processing and filtering data locally, transmitting only relevant information to the cloud.

Contributor: Isarsoft

With increased demand for IoT and cybersecurity, video analytics are being adopted by companies and businesses all over the world. Video analytics, which entail the use of live video data to generate insights into a particular domain, are largely enabled by AI to count, track and analyze data, building a model for present and predictive analysis through patterns:

Important to note is how edge analytics are gaining prominence. From our perspective, processing AI workloads on edge devices has three major advantages:
• A decentralized edge computing approach to AI lowers connectivity costs, especially when it comes to "heavy" video data.  

• Distributing AI workloads on industrial embedded computing devices reduces the risk for a critical workload to break at a central position.

• With industrial embedded computing data being produced on-site, data ownership stays local and data can be used as an input to other real-time systems running locally.

With advancements in video analytics, like software that can be deployed on-edge to quickly process and transmit critical information, today’s embedded designers can access enhanced functionality in each of these environments, with the resulting metadata sent to the cloud or other local systems. Edge deployments also offer round-the-clock analytics for time-critical eventualities, like passenger flow analysis in train stations, trains and buses.

Holger Heidenblut, Business Development Manager, Systems, Elma Electronic

The benefits of edge AI computing for applications that use video analytics and security for industrial settings are clearly stated above. Intrusion detection and access control, safety monitoring, and predictive equipment maintenance are good examples. From Elma’s perspective, there are key considerations in determining the selection of hardware and  integration partners:

1. Using open standards hardware architectures provides access to a wide range of boards that are interoperable and cross-compatible with common operating systems and interfaces.

2. By working with a team of partners with this ecosystem who each contribute their set of expertise, a solution tailored to the customer’s application saves significant time and costs, allowing them to focus on their core expertise.  In Elma’s case, we have decades of experience in designing rugged platforms and backplanes.

3. By putting the computing at the edge, it offers greater scalability at the device level. This offers more flexibility in the choice of an integrated system by tailoring it to the needs at the device level, improving and empowering operational efficiencies.  
Rugged edge AI systems for video analytics and security in industrial environments can enhance safety, improve operational efficiency, and proactively mitigate risks. The combination of powerful hardware, AI software frameworks, and real-time analysis on the edge empowers industrial facilities with advanced capabilities. By leveraging edge AI in industrial settings for video analytics and security, customers can achieve faster, more efficient solutions that are well-suited for the unique challenges and requirements of these environments.


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