Core Aspects

Embedded Machine Learning

Artificial Intelligence has the potential to process high-dimensional data very efficiently. We investigate how concepts of Deep Neural Networks or Convolusional Neural Netowrks can be applied to embedded systems.

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Programming Adaptive Systems

To cope with the rising performance requirements of future embedded systems we are developing new energy efficient hardware- and software solutions. We specifically focus on devices that incorporate reconfigurable and adaptive hardware.

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IoT Deployment and the Edge

IoT systems often consist of a great number of networked embedded devices combined with a number of software services in the Cloud and Edge. Management and control of such highly distributed systems is a interesting challenge for IoT systems.

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Embedded Security

IT security is a special challenge for embedded system, due to their limited amount of resources. A potential solution for IoT applications is Physically Unclonable Functions (PUF) on FPGAs.

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Network Function Virtualization

Future network architectures are expected to meet diverse service requirements. One way to overcome these challenges is to adopt Network Function Virtualizatio, Software-Defined Networking and Multi-access Edge Computing.

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Funded Projects


This project developes an innovative solution of deploying energy efficient Artificial Intelligence based on Artificial Neural Networks on Field Programmable Gate Arrays (FPGA). It aims to detect annomalies in ECG heart data.

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KI LiveS

The AI-laboratory for distributed and embedded systems has the goal to present and teach germany industry cutting edge research results about Artificial Intelligence.

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Photo by: eSeL - Joanna Pianka 2015,

Finished Projects


An overview of our publications can be found here.

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