Embedded AI Solutions for Real-World Applications

Edge AI brings together edge computing and artificial intelligence (AI) to process data right where it’s generated - directly on the device. Developing powerful embedded AI solutions, however, is about much more than training algorithms. It’s about designing the AI model, embedded software, and hardware to perform seamlessly as one unified system. That’s the challenge we take on.

Classic cloud-based AI depends on transferring large volumes of data to centralized servers — an approach that introduces delays, costs, and security concerns. For edge AI applications, intelligence must move closer to where data is created.

Embedded AI offers significant advantages in these scenarios: Low Latency enables real-time responses for robotics and automation. Data Privacy ensures that sensitive information remains on the device. Energy Efficiency reduces power consumption in mobile and remote systems. Autonomy allows edge devices to operate safely without constant connectivity, while Information Condensing limits data transmission to the most relevant insights.

Building on these strengths, we have addressed the remaining integration challenges, such as aligning AI models, software stacks and hardware architectures, by developing our Reference Design Platform. This platform enables the rapid adaptation of Embedded AI for use in real-world applications.

The Fraunhofer EMFT Machine Learning Enhanced Sensor Systems group has developed a reference design platform to overcome integration challenges and quickly adapt embedded AI to real-world applications. The platform addresses key challenges of decentralized sensor networks.  By combining configurable hardware and software building blocks, the reference design supports the development, evaluation, and deployment of intelligent sensor systems for industrial and research environments.

Our platform includes the following features:

Versatile Hardware

An STM32 microcontroller-based embedded system offering generic sensor connectivity via various interfaces. It can be deployed as a self-contained, battery-powered wireless node utilizing LTE-M/NB-IoT for lightweight communication.

Advanced Pre-Processing

Application specific pre-processing is the key for Embedded AI. Because edge devices operate under strict resource constraints, our pipeline systematically structures and cleans raw sensor data before inference, which is critical for maximizing on-device performance.

Scalable, Optimized Models

The platform supports everything from simple linear models to complex deep neural network architectures. To overcome the compute limitations of the edge, we actively employ optimization methods like quantization and pruning to shrink memory and computational requirements with minimal sacrifice in accuracy.

Complete Data Pipeline

Our server-side backend is a fully Dockerized environment built on best-practice open-source software. It gathers and stores incoming sensor data in a time-series database, providing the foundation for server-side performance evaluation and the continuous improvement of machine learning models.

Leverage our expert Edge AI and a modular, customer-specific reference design platform at Fraunhofer EMFT. We are eager to collaborate and innovate with you - contact us!