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Department of Computer Science
Design Automation for Embedded Systems Group

M.Sc. Mikail Yayla

Mikail Yayla left the DAES group in September 2023 and received his Dr.-Ing. degree in Feb. 2024.


E-Mail: mikail.yaylatu-dortmundde
Phone: +49 231 755-6110
Fax: +49 231 755-6116



Mikail Yayla
Technische Universität Dortmund
Lehrstuhl Informatik 12
Otto-Hahn-Str. 16
44227 Dortmund
Room E23

 M.Sc. Mikail Yayla © M.Sc. Mikail Yayla

Research Interests

  • Robust and Efficient Machine Learning for Embedded Systems
    • Neural Networks
    • Decision Trees and Random Forests
  • Emerging Low-Power Hardware
    • Digital and Analog Computing-based Accelerators
    • Neuromorphic Accelerators
    • Devices with Approximate Components (Memory, MAC-units, etc.)



  • MLCAD 2019 (Subreviewer)
  • ISLPED 2020, 2021 (Subreviewer)
  • ICCAD 2022 (Subreviewer)
  • MDPI Sensors

Supervised Theses:

  • Efficient Training of Binarized Neural Networks through Gradient Information Drop (ongoing, Sanad Marji)
  • Design and Implementation of a Fixed Point FFT Accelerator for Real-time Quantum Spin Noise Spectroscopy (ongoing, Dominik Riemer)
  • Optimization of Hardware Design for Real-time Quantum Spin Noise Spectroscopy (completed by Sebastian Hilker)
  • Design and Evaluation of Accelerator Organizations for Binarzed Neural Networks (completed by Somar Iskif)
  • Efficient online learning in Spiking Neural Networks for drifting data streams (completed by  Florian Köhler)
  • GPU Acceleration for the Inference of Binarized Neural Networks (completed by Leonard Bereholschi)
  • Design and Evaluation of Superscalar Processor Organizations for Single-Cycle and pipelined Architectures (completed by Robert Huber)
  • Gradient-based Pruning of Binarized Neural Networks (completed by Ole Dickel)
  • Hardware Acceleration for Real-time Quantum Spin Noise Spectroscopy (completed by Kevin Goldstein)
  • Optimization of Processing Elements for Binarized Neural Networks (completed by Umut Yilmaz)

Good News

  • Best Paper Nomination at DATE 2021
  • SIGDA 2019 MLCAD Student Travel Grant