Embedded AI & Industry 4.0: Opportunities & Challenges

Table of contents

1. What is embedded AI?
2. AI sensors: in high demand
3. Opportunities: Where is embedded AI used?
4. Challenges of embedded AI in Industry 4.0
5. Outlook for the future: neuromorphic semiconductors & embedded AI
6. Embedded AI – a technology with real power

What is embedded AI?

Embedded AI refers to the integration of artificial intelligence directly into hardware and systems. These embedded AI models work decentrally and enable real-time data processing on site without the need for cloud services. This shortens response times and increases security – which is of great importance for Industry 4.0.

AI sensors: in high demand

Advancing technological development plays a crucial role in the integration of embedded AI into the processes of Industry 4.0. Modern AI sensors, for example, are essential for real-time monitoring and control of production processes. The recent easing of the semiconductor supply situation is facilitating the production and implementation of such intelligent sensors.

Their ability to capture and analyse complex data in real time enables companies to make significant cost savings. This is because they can respond more quickly to deviations, optimise maintenance work and make the entire production chain more efficient.

Opportunities: Where is embedded AI used?

Faster, better, cheaper: efficiency has always played a major role in industrial manufacturing processes. In the ideal case, clever production saves not only time but also human and machine resources, thus achieving immense cost savings. Embedded AI is designed to support precisely these efforts and is therefore gaining in importance in many respects.

Embedded AI optimises predictive maintenance

Predictive maintenance is considered a promising method for permanently reducing maintenance costs while at the same time increasing the lifespan of the equipment. The key here is that production facilities are equipped with intelligent sensors. These provide continuous and real-time insights into the condition of machines and buildings.

This type of monitoring helps to detect anomalies, prevent failures and make maintenance processes more plannable. Embedded AI can be used to further optimise predictive maintenance processes and to derive predictions about potential error development. The result: operational efficiency and thus also the competitiveness of the company increase.

Embedded AI favours user interaction in Industry 4.0

Industry has a personnel problem. In numerous sectors worldwide, there is a lack of qualified specialists and there has been no positive change in this development so far. However, embedded AI is sowing hope. It is able to recognise human gestures and their spoken word and process them immediately.

So-called ‘automated cobots’, i.e. robots that have been specially developed to work directly with humans, combine user interaction with artificial intelligence. Controlled remotely by a supervisor, the intelligent cobots can now assist with various tasks or perform them alone if the necessary skilled workers are not available.

Embedded AI improves healthcare

In the medical sector, embedded AI has great potential to significantly improve patient well-being. Data protection-compliant analyses of movement patterns on the body, in the face or even the sound of the voice make more objective diagnoses possible. Strokes and heart attacks can thus be detected at an early stage. Intelligent wearables also continuously record, monitor and analyse vital parameters. All these AI-supported measures favour an immediate and precise response, improve patient care and significantly reduce treatment costs.

Embedded AI is boosting the aviation industry

The aviation industry is also looking forward to embedded AI-based solutions. It seems that predictive maintenance could soon optimise various operational processes – both on the airport premises and in the aircraft itself, in terms of cabin comfort. Once again, the focus is on numerous sensors that, for example, detect stressed people with a safety risk, ensure that suitcases are transported to the runway or check engine efficiency.

Other types of sensors monitor the condition of critical components on board aircraft in real time and use AI to predict maintenance needs. These advances should contribute to a safer and more comfortable flying experience in the future.

Challenges of embedded AI in Industry 4.0

Despite the numerous opportunities, the integration of embedded AI also brings with it various challenges. These must be taken into account and addressed so that the technology can be used profitably and safely in Industry 4.0.

• Complexity of implementation: The integration of AI into existing systems requires appropriate technical resources and considerable development effort. In many cases, higher demands are placed on the interfaces, which means that some production facilities often have to be completely converted, which can lead to costs and downtime. One example is the adaptation of an existing production line for automotive assembly, in which new AI-based sensors and actuators have to be installed.
• Technological dependence: If manufacturing processes rely exclusively on embedded AI systems, a problematic form of dependence can arise. In semiconductor manufacturing, for example, the failure of an AI system to monitor clean room conditions could jeopardise the entire production process.
• Ethics and responsibility: The decision-making authority of AI systems in critical processes raises ethical questions. One example is the use of AI in autonomous vehicle control, where it is difficult to clarify responsibilities in the event of an accident.

Outlook for the future: neuromorphic semiconductors & embedded AI

Neuromorphic semiconductors are electronic components that mimic the way the human brain works. This technology is increasingly being used in embedded AI systems, especially for processing sensor information in robots or for speech recognition.

By integrating neuromorphic semiconductors into embedded AI systems, developers can utilise the advantages of both technologies to create more powerful, efficient and adaptable devices. In terms of energy efficiency in particular, neuromorphic semiconductors in combination with embedded AI are considered to be the industry’s great white hope.

Embedded AI – a technology with real power

Our preliminary conclusion on embedded AI: many opportunities, some challenges and certainly still a great deal of development potential. In the field of Industry 4.0 in particular, the technology is heralding a turnaround, and it remains to be seen to what extent and how quickly these changes will be felt in manufacturing.

Would you like to learn more about the digitalization of industrial production? Then browse through our Industry 4.0 category!