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Martin Pagel

Mo 03.02.2020, 16 – 17 Uhr TU Berlin, EN building, seminar room EN 719 (7th floor), Einsteinufer 17, 10587 Berlin: "Hysteretic neural networks, stream processing system on modern hardware, and research agenda" (Zongxiong Chen)

This presentation consists of three parts.
First, I will present my master thesis work and introduce Hysteretic neural networks (HNN).
HNNs are a new architecture of recurrent neural networks (RNN), which is designed to approximate dynamic systems based on hysteresis.
In my work, I showed that HNN models hysteretic systems better than the state-of-the-art approaches such as LSTM. Furthermore, HNNs can capture the micro-loops inside hysteretic behaviors, whereas LSTM fails.
In the second part of my presentation, I will present my work on benchmarking stream processing systems (SPSs) for modern hardware. SPSs such as Streambox and Saber achieve high performance by exploiting the parallelism and memory hierarchy of modern multicore hardware.
In my presentation, I will compare the architecture of both systems and present our evaluation results.
Finally, I will give an overview of my future research agenda.