Research
Adaptive and Intelligent Embedded Systems
The goal of the Intelligent Embedded Systems lab's research 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 Industry 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.
Core Aspects
Optimizing Deep Learning Models
Artificial Intelligence has the potential to process high-dimensional data very efficiently. We investigate how concepts of Deep Neural Networks or Convolutional Neural Networks can be applied to embedded systems.
AI Hardware Acceleration
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.
Previous Research
In our research archive you will find an overview of all previous research foci of the Intelligent Embedded Systems Lab.