From Quantitative Analysis to Synthesis of Efficient Binary Neural Networks. - Tim Stadtmann; Cecilia Latotzke; Tobias Gemmeke (ICMLA, 2020)

Communication architecture enabling 100x accelerated simulation of biological neural networks. - Kevin Kauth; Tim Stadtmann; Ruben Brandhofer; Vida Sobhani; Tobias Gemmeke (SLIP, 2020)

All-Digital Time-Domain Compute-in-Memory Engine for Binary Neural Networks With 1.05 POPS/W Energy Efficiency. - Jie Lou; Christian Lanius; Florian Freye; Tim Stadtmann; Tobias Gemmeke (ESSCIRC, 2022)

Deadlock-Freedom in Computational Neuroscience Simulators. - Vida Sobhani; Kevin Kauth; Tim Stadtmann; Tobias Gemmeke (IEEE Des. Test, 2022)

NoisyDECOLLE: Robust Local Learning for SNNs on Neuromorphic Hardware. - Tim Stadtmann; Benedikt Wahl; Tobias Gemmeke (ICMLA, 2023)

neuroAIx: FPGA Cluster for Reproducible and Accelerated Neuroscience Simulations of SNNs. - Kevin Kauth; Tim Stadtmann; Vida Sobhani; Tobias Gemmeke (NorCAS, 2023)

A Digital Twin Network for Computational Neuroscience Simulators: Exploring Network Architectures for Acceleration of Biological Neural Network Simulations. - Vida Sobhani; Kevin Kauth; Tim Stadtmann; Tobias Gemmeke (WoWMoM, 2023)

neuroAIx-Framework: design of future neuroscience simulation systems exhibiting execution of the cortical microcircuit model 20× faster than biological real-time. - Kevin Kauth; Tim Stadtmann; Vida Sobhani; Tobias Gemmeke (Frontiers Comput. Neurosci., 2023)

Constructive community race: full-density spiking neural network model drives neuromorphic computing. - Johanna Senk; Anno Christopher Kurth; Steve B. Furber; Tobias Gemmeke; Bruno Golosio; Arne Heittmann; James C. Knight; Eric Müller 001; Tobias Noll; Thomas Nowotny; Gorka Peraza Coppola; Luca Peres; et al. (CoRR, 2025)