Artificial intelligence in embedded systems
Artificial intelligence has found its way into many areas to make systems more precise and reliable than previous manually optimized algorithms. One common method is machine learning, which learns to solve predefined tasks on its own based on a large database. But for artificial intelligence to enrich the end application, it must be embedded. A particular challenge here is to compress the networks in such a way that, on the one hand, energy efficiency on the target platform increases and, on the other hand, accuracy is maintained.
During training, many athletes already wear fitness trackers to record body parameters, step counts or daily activity. The ITK Innovation & Venture Lab is going one step further and researching smart textiles for motion detection and assessment. For this purpose, inertial measurement units (IMUs) for recording upper body movement have already been textile-integrated into a shirt. With an eye toward protecting private data while keeping power consumption low in the shirt, edge versus remote solutions are weighed against each other.
Many people strive for a long, self-determined life within their own four walls. However, the care crisis means that human care in the home is becoming increasingly rare. As an alternative, KIT is researching assistive robots that help elderly people to cope with everyday life at home. Important aspects for increasing their acceptance are short response times and personalization of the services. For this purpose, ITIV is researching suitable hardware accelerators with low latencies and a high degree of privacy.
|Development of a Hardware Accelerator for Graph Neural Networks for Object Recognition in Embedded Systems||Master thesis|
|Energy Efficient Smart Cities with ESP32 IoT WiFi Sensing||Bachelor-/ Masterarbeit / HiWi|
|Design and Implementation of a Hardware Accelerator for Deep Neural Networks for Face Recognition on FPGA||Bachelor-/ Masterarbeit|
Supervised student works (selection)
- MA: “Konzeptioneller Entwurf eines modularen Sensornetzwerks für intelligente Textilanwendungen”
- MA: „Datenanalyse von Sensorinformationen in intelligenten Textilanwendungen“
Kreß, F.; Hoefer, J.; Hotfilter, T.; Walter, I.; Sidorenko, V.; Harbaum, T.; Becker, J.
2022. 18th International Conference on Distributed Computing in Sensor Systems (DCOSS), 133–140, IEEEXplore. doi:10.1109/DCOSS54816.2022.00034
Walter, I.; Ney, J.; Hotfilter, T.; Rybalkin, V.; Hoefer, J.; Wehn, N.; Becker, J.
2022. Machine Learning and Principles and Practice of Knowledge Discovery in Databases – International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Ed.: M. Kamp, 339–350, Springer International Publishing. doi:10.1007/978-3-030-93736-2_26