Minimum energy quantized neural networks. - Bert Moons; Koen Goetschalckx; Nick Van Berckelaer; Marian Verhelst (ACSSC, 2017)

Efficiently Combining SVD, Pruning, Clustering and Retraining for Enhanced Neural Network Compression. - Koen Goetschalckx; Bert Moons; Patrick Wambacq; Marian Verhelst (EMDL@MobiSys, 2018)

Optimized Hierarchical Cascaded Processing. - Koen Goetschalckx; Bert Moons; Steven Lauwereins; Martin Andraud; Marian Verhelst (IEEE J. Emerg. Sel. Topics Circuits Syst., 2018)

Breaking High-Resolution CNN Bandwidth Barriers With Enhanced Depth-First Execution. - Koen Goetschalckx; Marian Verhelst (IEEE J. Emerg. Sel. Topics Circuits Syst., 2019)

DepFiN: A 12nm, 3.8TOPs depth-first CNN processor for high res. image processing. - Koen Goetschalckx; Marian Verhelst (VLSI Circuits, 2021)

DeFiNES: A DSE Framework Enabling Fast Exploration of the Depth-first Scheduling Space for DNN Accelerators. - Linyan Mei; Koen Goetschalckx; Arne Symons; Marian Verhelst (Zenodo, 2022)

DeFiNES: Enabling Fast Exploration of the Depth-first Scheduling Space for DNN Accelerators through Analytical Modeling. - Linyan Mei; Koen Goetschalckx; Arne Symons; Marian Verhelst (HPCA, 2023)

DepFiN: A 12-nm Depth-First, High-Resolution CNN Processor for IO-Efficient Inference. - Koen Goetschalckx; Fengfeng Wu; Marian Verhelst (IEEE J. Solid State Circuits, 2023)

11.3 Metis AIPU: A 12nm 15TOPS/W 209.6TOPS SoC for Cost- and Energy-Efficient Inference at the Edge. - Pascal Alexander Hager; Bert Moons; Stefan Cosemans; Ioannis A. Papistas; Bram Rooseleer; Jeroen Van Loon; Roel Uytterhoeven; Florian Zaruba; Spyridoula Koumousi; Milos Stanisavljevic; Stefan Mach; Sebastiaan Mutsaards; et al. (ISSCC, 2024)