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

Cascaded Classifier for Pareto-Optimal Accuracy-Cost Trade-Off Using Off-the-Shelf ANNs. - Cecilia Latotzke; Johnson Loh; Tobias Gemmeke (LOD, 2021)

Efficiency Versus Accuracy: A Review of Design Techniques for DNN Hardware Accelerators. - Cecilia Latotzke; Tobias Gemmeke (IEEE Access, 2021)

Design of High-Throughput Mixed-Precision CNN Accelerators on FPGA. - Cecilia Latotzke; Tim Ciesielski; Tobias Gemmeke (FPL, 2022)

Post-Training Quantization for Energy Efficient Realization of Deep Neural Networks. - Cecilia Latotzke; Batuhan Balim; Tobias Gemmeke (ICMLA, 2022)

FPGA-based Acceleration of Lidar Point Cloud Processing and Detection on the Edge. - Cecilia Latotzke; Amarin Kloeker; Simon Schoening; Fabian Kemper 002; Mazen Slimi; Lutz Eckstein; Tobias Gemmeke (IV, 2023)

DAEBI: A Tool for Data Flow and Architecture Explorations of Binary Neural Network Accelerators. - Mikail Yayla; Cecilia Latotzke; Robert Huber; Somar Iskif; Tobias Gemmeke; Jian-Jia Chen (SAMOS, 2023)

Co-optimization of neural networks and hardware architectures for their efficient execution. - Cecilia Latotzke (2023)