Search Space Complexity of Iteration Domain Based Instruction Embedding for Deep Learning Accelerators. - Dennis Rieber; Holger Fröning (IoT Streams/ITEM@PKDD/ECML, 2020)

The Programming of Deep Learning Accelerators as a Constraint Satisfaction Problem. - Dennis Rieber; Axel Acosta 001; Holger Fröning (CoRR, 2021)

HW-Aware Initialization of DNN Auto-Tuning to Improve Exploration Time and Robustness. - Dennis Rieber; Moritz Reiber; Oliver Bringmann 001; Holger Fröning (CoRR, 2022)

Joint Program and Layout Transformations to Enable Convolutional Operators on Specialized Hardware Based on Constraint Programming. - Dennis Rieber; Axel Acosta 001; Holger Fröning (ACM Trans. Archit. Code Optim., 2022)

Simulation-driven Latency Estimations for Multi-core Machine Learning Accelerators. - Yannick Braatz; Dennis Sebastian Rieber; Taha Soliman; Oliver Bringmann 001 (AICAS, 2023)

SimPyler: A Compiler-Based Simulation Framework for Machine Learning Accelerators. - Yannick Braatz; Dennis Sebastian Rieber; Taha Soliman; Oliver Bringmann 001 (ASAP, 2023)

Deployment of Deep Neural Networks on Dedicated Hardware Accelerators. - Dennis Rieber (2023)

CoNAX: Towards Comprehensive Co-Design Neural Architecture Search Using HW Abstractions. - Yannick Braatz; Taha Soliman; Shubham Rai; Dennis Sebastian Rieber; Oliver Bringmann 001 (ASAP, 2024)