The goal of the Embedded Systems research group is to develop algorithms, concepts and procedures to develop networked embedded systems. Examples for this are the so called Internet of Things (IoT), Cyber-physical Systems (CPS) or Industrie 4.0. To cope with the requirements of future IoT-applications we research the applicability of reconfigurable and adaptive hardware, as well as the usage of AI-technologies on resource constrained systems. The focus lies on Deep Learning and Deep- and Convolutional Neural Networks for embedded systems.
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.
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.
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.
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.
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.