Leading the Way with Embedded AI

In the 6G LINO project, Fraunhofer IIS is working with partners from the European satellite and mobile communications industry to develop a test infrastructure for testing 6G mobile communications via a LEO satellite. The project, which is funded by the European Space Agency (ESA), involves setting up a complete transmission chain from the ground station via a satellite in CubeSat format to the receiver on the ground.

Various features are being implemented and demonstrated in the project, including a mobile base station (gNB) on the satellite, handover between terrestrial and non-terrestrial network, options for testing future 6G waveform adaptations and spectrum sensing with AI. To this end, an AI-based solution will be integrated on the satellite to examine the utilization of frequencies in orbit, for example to optimize the use of the spectrum (dynamic satellite-terrestrial spectrum management and sharing in S-band). Fraunhofer IIS is working on implementing the planned functions and preparing everything for their deployment on the satellite.

As part of the GAIA Initiative, Fraunhofer IIS and its partners have developed a high-tech early warning system that uses animal transmitters to detect ecological changes and critical events in the environment. The initiative comprises the two sub-projects GAIA-Sat-IoT and SyNaKI. They were funded by the Federal Ministry for Economic Affairs and Climate Action on the basis of a decision by the German Bundestag.

In the GAIA-Sat-IoT project, intelligent camera tags with integrated AI and satellite-based IoT communication were developed to collect ecological data and for the early detection of wildlife diseases.

In the SyNaKI project, Distriubted AI was used to create virtual swarm intelligence based on the interaction of animals and microprocessors in digital networks. 

In the KI-FLEX project, funded by the German Federal Ministry of Education and Research, eight project partners developed a high-performance, energy-efficient hardware platform and the associated software framework for autonomous driving. The KI-FLEX platform is designed to reliably and quickly process and merge data from laser, camera and radar sensors in the car. Artificial intelligence (AI) methods are used for this purpose. The vehicle thus always has an accurate picture of the actual traffic conditions, can locate its own position in this environment, and on the basis of this information, make the right decision in every driving situation.

Fraunhofer IIS's contribution was the development of a flexible DLI accelerator core for the multi-core deep learning accelerator, which is integrated together with other DLI accelerators into a flexible, future-proof ASIC. The architecture of the ASIC is designed in such a way that future improvements of neural network (NN)architectures, i.e. newly emerging NN types and concepts, can still be realized with it. For this purpose, critical areas were specifically designed to be reconfigurable in order to build a bridge from the rigidity of an ASIC to the flexibility of an FPGA.

The LODRIC project, funded by the Federal Ministry of Education and Research, extended the successful collaboration of the consortium, consisting of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer IIS, from the previous project Lo3-ML.

Its continuation is about the development of a design methodology for low-power digital AI chips with embedded non-volatile memory elements and its prototypical application on the basis of three different applications. Thereby, the main innovation of the project Lo3-ML, namely the development of data flow-oriented computer architectures in combination with distributed, non-volatile weight memory and strongly (ternary) quantized weights shall be taken up and specifically methodically developed further.

Fraunhofer IIS is represented with three disciplines: medical technology, digital circuit design and Embedded AI. The latter will expand its competencies in the area of hardware-aware training. In this context, a tool chain specific to accelerator technology will be further developed, which on the one hand achieves a significant reduction (optimization) of the neural network and on the other hand maintains its accuracy despite high quantization of the neuron weights through iterative retraining.

The vision of MANOLO is to deliver a complete and trustworthy stack of algorithms and tools to help AI systems reach better efficiency and seamless optimization in their operations. The focus is on the energy-efficient training of AI models with quality-checked data and the execution of resource-efficient AI models on a wide range of devices for use on the edge and in the cloud. MANOLO is funded by the Horizon Europe framework programme of the European Union.

In the project, Fraunhofer IIS is focusing on bringing AI applications to the edge. To this end, algorithms and tools are being developed that search for and optimize suitable neural networks automatically (Neural Architecture Search, NAS).