Etienne Müller is a research assistant and PhD candidate since 2018. He received his Master's degree in Product Development and his Bachelor's Degree in Mechanical Engineering at the Hamburg University of Technology in 2017 and 2014, respectively.
Etienne's current research topic is the development of spiking neural networks in the context of path planning and decision making.
Conversion of today's commonly used analog neural network to spiking neural network for the usage in neuromorphic computing.
Master Thesis (2021): Performance of Time to First Spike Encoded Spiking Neural Networks
Guided Research (2021): Conversion of TransformerNets
Master Thesis (2021): Conversion of LSTM-based Recurrent Neural Networks
Master Thesis (2021): Conversion of GRU-based Recurrent Neural Networks
Research Internship (2020): Carla as Open Source Platform for Analyzing and Evaluating Autonomous Driving
Master Thesis (2020):Converting Analogue to Spiking Convolutional Neural Networks for Object Detection
Graduation Thesis (2019):Semantic Segmentation of Integrated Circuit Layout Images
Mueller, Etienne; Auge, Daniel; Klimaschka, Simon; Knoll, Alois: Neural Oscillations for Energy-Efficient Hardware Implementation of Sparsely Activated Deep Spiking Neural Networks. Association for the Advancement of Artificial Intelligence (AAAI), 2022Practical Deep Learning in the Wildmehr…BibTeX
Mueller, Etienne; Auge, Daniel; Knoll, Alois: Exploiting Inhomogeneities of Subthreshold Transistors as Populations of Spiking Neurons. International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2022 mehr…BibTeX
Daniel Auge, Julian Hille, Etienne Mueller, Alois Knoll: Hand Gesture Recognition in Range-Doppler Images Using Binary Activated Spiking Neural Networks. IEEE International Conference on Automatic Face and Gesture Recognition 2021, 2021acceptedmehr…BibTeX
Daniel Auge, Julian Hille, Felix Kreutz, Etienne Mueller, Alois Knoll: End-to-end Spiking Neural Network for Speech Recognition Using Resonating Input Neurons. 30th International Conference on Artificial Neural Networks (ICANN), 2021acceptedmehr…BibTeX
Etienne Mueller, Julius Hansjakob, Daniel Auge, Alois Knoll: Minimizing Inference Time: Optimization Methods for Converted Deep Spiking Neural Networks. International Joint Conference on Neural Networks (IJCNN), 2021acceptedmehr…BibTeX
Mueller, Etienne; Studenyak, Viktor; Auge, Daniel; Knoll, Alois: Spiking Transformer Networks: A Rate Coded Approach for Processing Sequential Data. Internation Conference on Systems and Informatics (ICSAI), 2021acceptedmehr…BibTeX
Auge D, Wenner P, Mueller E: Hand Gesture Recognition using Hierarchical Temporal Memory on Radar Sequence Data. Bernstein Conference 2020, 2020 mehr…BibTeX
Daniel Auge, Etienne Mueller: Resonate-and-Fire Neurons as Frequency Selective Input Encoders for Spiking Neural Networks. 2020, mehr…BibTeX
Mueller E, Hansjakob J, Auge D: Faster Conversion of Analog to Spiking Neural Networks by Error Centering. Bernstein Conference 2020, 2020 mehr…BibTeX