M.Sc. Mikail Yayla

• 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.)

Reviewer:

• ACM TODAES
• 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)

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