Embedded AI is bringing about a new age of intelligent computing. Embedded AI refers to electronic systems in which artificial intelligence (AI) works independently and locally. Embedded AI is a younger field that continues Edge AI but with complete autonomy. The system runs locally, for example, on a circuit board sensor and acts on-site in real-time without a network or large data transfers.
This trend towards decentralization makes it possible to equip every device – from household to industry to mobility – with artificial intelligence. The market potential is enormous – partly caused by follower trends such as (I)IoT, connectivity, security and cloud services.
According to an analysis by DAC, the global embedded AI market will grow by 5.4 percent annually until 2026, when the market volume will reach the 40 billion US dollar mark. In the same period, the AI-enabled semiconductor chips will increase to a market volume of around 125 billion US dollars with an annual growth rate of around 34 percent.
Diverse Areas Of Application
Embedded AI is only at the beginning of its development potential, so at the current stage, this gives every product at least one unique selling point (USP). Use and benefit for the manufacturer and user must be in harmony with one another.
With embedded AI, improvements can be achieved and new functions created, especially in mechanical engineering, medicine, consumers, and automotive, as well as in production and manufacturing. Suppose companies make further progress with this technology. In that case, they will master the current global crisis-competitive situation by using predictive maintenance to prevent failures. They can enter new, continuous business models based on use instead of unit sales.
Examples Of Embedded AI Innovations
- A stove that ensures that food is cooked optimally (keyword: guided cooking)
- A toothbrush recognizes the tooth status and informs the user about it at an early stage. For example, the number of necessary visits to the dentist can be optimized.
- A room filter that automatically flexibly adjusts operation depending on whether the window is open.
- A washing machine that, depending on the load and wear (“state of health”), washes more efficiently or calls for service independently.
- Therapy and surgical devices can be controlled by the surgeon directly by voice without needing an assistant nurse.
- Room lighting automatically adjusts the intensity and type of light depending on the surgical phase, thanks to image recognition of the surgical site.
- Snoring therapy devices that detect plumbing abnormalities through redundant calorimetric sensors and indicate a leak or changing breathing intensity
- A ropeway that recognizes the number of people for capacity utilization and cable vibration for system safety.
- Obstacle detection in AGVS using lidar technology with trajectory suggestion (e.g. wheel blocked by a stone).
- A turbine that automatically adjusts to the eco-kerosene blends for longer life.
- The vehicle powertrain is monitored with predictive maintenance for safety.
- Industrial air cleaning systems in which the washing water is assessed at an early stage using spectrography for the best cleaning result.
- A transport system that warns of production standstills in the entire system in good time using the fluttering belt.
- An injection molding machine that adjusts the process in real time based on pressure and temperature data to achieve a perfect result.
- A metal separator switches off for safety reasons when the operator calls for an audible “emergency stop”.
The Benefits Of Embedded AI
Embedded AI enables large amounts of data to be processed locally, reducing the risk of sensitive data being intercepted or tampered with. This leads to higher data and system security. A device can provide a high-performance network infrastructure to process data. Thus, less connectivity is required, which reduces production costs. Embedded AI lives on limited resources regarding power supply (including battery operation), computing and storage power. Such components collect and process the data immediately and can react to it in milliseconds, which is a must in many applications. The device can also analyze data in real-time and only transmits what is relevant for further analysis in the cloud (keyword: reduce data volumes).
Here you will find tailor-made embedded AI solutions for specific challenges and specifications of the manufacturers. In practice, there are three main areas of application for embedded AI:
Functional Innovations: new functions that optimize or even change the target use of a product or process.
User Interaction: This ranges from simple voice command input (i.e. KWS, Keyword Spotting) to gesture recognition to more complex human-machine collaborations such as operator tracking, eye tracking or workpiece detection.
Predictive/Preventive Maintenance: This is about intelligent, predictive maintenance that goes beyond simple condition monitoring and provides reliable predictions about specific error patterns at an early stage.
Where Will The Embedded AI Trend Go?
Because conclusions, predictions and imitations are becoming more and more perfect due to increasing computing power, better machine learning models and existing amounts of data, there will be further progress and upheavals.
The systems – the stove, the car or the machine in production – are becoming increasingly intelligent. Due to the increasing decentralization and self-sufficiency with embedded AI, central computing systems such as servers and the cloud will only perform overarching context tasks. Incidentally, this type of decentralization and efficiency can also be observed.
Speech or person recognition, for example, is a lot about command control or person detection. But new technologies such as organic semiconductors, memristor arrays and spiking neural networks, which are closer to biological neural networks than other types of networks, will help shape the future. Then it will be possible to recognize emotions or entire clinical pictures based on language or movement.
In medicine, data analysis using embedded AI will, in future, make it possible to predict the probability of occurrence of certain diseases and recovery processes to a much more comprehensive extent than is the case today. Embedded AI will bring intelligence to devices in the home, enterprise, and manufacturing environment, interacting with the user on a functional and collaborative level and offering new capabilities.
Megatrend sustainability: This technology will also change many business models in the economy and thus contribute to more sustainability: With models such as “Hardware-as-a-Service”, for example, intelligent, long-lasting devices can only be rented, whereby they automatically reorder consumables and use them wait early.
Rely On Individual Solutions
The embedded AI market still needs to be more occupied, with more and more isolated solutions or low-threshold offers springing up. Specific solutions (often also closed-source) can be an advantage for the company in individual cases and if integration occurs at an early stage. Low-threshold software offers from a wide variety of semiconductor manufacturers or more comprehensive tools such as “Edge Impulse” are both a blessing and a curse: you get results quickly (also partly thanks to AutoML functionality, i.e. automated model creation process). However, the entire development chain, which depends on the understanding of the respective developer, is limited.
The semiconductor industry offers a range of chips tailored to embedded AI use cases, such as image processing, which are performant and adapted. But even this diversification of the hardware market brings with it confusion.
Ready-made isolated solutions can only be adapted to the needs to a limited extent, with more minor or significant cuts. Companies should prefer individual solutions, as custom-made systems offer significantly more scope.
It’s about finding out which AI model fits into the product, how it can be effectively implemented on hardware, developing the appropriate system components based on collected and evaluated data, implementing the whole thing using a prototype and testing it in practice. That sounds like a lot of effort at first. If companies take a closer look at how long the product has been on the market and what advantages companies and users have from it, for example, in preventive/predictive maintenance, the investment is worthwhile.