Research
Core Aspects
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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.
Funded Projects
![elastic AI](/imperia/md/images/es/margin_600_337_7885f9a94fde8b658accdb159010afaf_elastic_ai.png)
elastic AI
The elastic AI ecosystem creates development tools and runtime environments for both embedded and distributed AI applications.
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Neural signal processing using artificial intelligence on an embedded platform (Sp:AI:ke)
How can AI be used in medical implants? The MERCUR project "Neural signal processing using artificial intelligence on an embedded platform" or Sp:AI:ke for short is tackling this question.
![past projects](/imperia/md/images/es/past_projects.jpg)
Completed Projects
- DAAD-funded Summer School in Armenia (2022)
- KI-LiveS (2019-2022)
- LUTNet (2019-2020)
- FiPS (2013-2016)
- Third Life Project (2015)