The project group EML II consists three group of tasks:
- We will study the approximation of ML techniques to be able to cope with resource limitations. Approximation can be done at the algorithm level, like employing sparsity techniques to reduce the number of underlying computations. Approximation can also be supported by the hardware level, for example by reduced precision data types or inaccurate arithmetic components. Both types of approximations trade off reductions in energy and hardware for decreased ML accuracy.
- We will investigate the ML algorithms for anomaly detection to monitor the quality of the wind turbines and measure their performances on an industrial dataset under several failure senarios. Here, the dataset preprocessing also is performed on the available dataset to make the ready for AD algorithms.
- We will employ modern reconfigurable systems-on-chip platforms for hardware implementation. Such platforms, for example the Xilinx UltraScale+MPSoC, provide a combination of different ARM CPU cores (64 and 32 bit), an embedded GPU, reconfigurable hardware, and memory and peripherals. We will implement the verified algorithm from previous task sets on such a heterogeneous platform and leverage required optimizations as well.
The project group will be done in cooperation with Weidmüller. We will discuss our goals and developments with industry and demonstrate the performance of our EML techniques on real industrial data provided by Weidmüller. In the course of the project, we will also schedule an excursion to a Weidmüller production site.
For more information, please check out the slide set from the project group presentation or contact the advisors: