Tumor Segmentation from Multimodal MRI Using Random Forest with Superpixel and Tensor Based Feature Extraction. - Halandur Nagaraja Bharath; Steven Colleman; Diana Maria Sima; Sabine Van Huffel (BrainLes@MICCAI, 2017)

Processor Architecture Optimization for Spatially Dynamic Neural Networks. - Steven Colleman; Thomas Verelst; Linyan Mei; Tinne Tuytelaars; Marian Verhelst (VLSI-SoC, 2021)

High-Utilization, High-Flexibility Depth-First CNN Coprocessor for Image Pixel Processing on FPGA. - Steven Colleman; Marian Verhelst (IEEE Trans. Very Large Scale Integr. Syst., 2021)

Optimizing Accelerator Configurability for Mobile Transformer Networks. - Steven Colleman; Peter Zhu; Wei Sun; Marian Verhelst (AICAS, 2022)

Towards Heterogeneous Multi-core Accelerators Exploiting Fine-grained Scheduling of Layer-Fused Deep Neural Networks. - Arne Symons; Linyan Mei; Steven Colleman; Pouya Houshmand; Sebastian Karl; Marian Verhelst (CoRR, 2022)

Stream: A Modeling Framework for Fine-grained Layer Fusion on Multi-core DNN Accelerators. - Arne Symons; Linyan Mei; Steven Colleman; Pouya Houshmand; Sebastian Karl; Marian Verhelst (ISPASS, 2023)

CMDS: Cross-layer Dataflow Optimization for DNN Accelerators Exploiting Multi-bank Memories. - Man Shi; Steven Colleman; Charlotte VanDeMieroop; Antony Joseph; Maurice Meijer; Wim Dehaene; Marian Verhelst (ISQED, 2023)

COAC: Cross-Layer Optimization of Accelerator Configurability for Efficient CNN Processing. - Steven Colleman; Man Shi; Marian Verhelst (IEEE Trans. Very Large Scale Integr. Syst., 2023)

Optimizing Layer-Fused Scheduling of Transformer Networks on Multi-accelerator Platforms. - Steven Colleman; Arne Symons; Victor J. B. Jung; Marian Verhelst (ISQED, 2024)

Stream: Design Space Exploration of Layer-Fused DNNs on Heterogeneous Dataflow Accelerators. - Arne Symons; Linyan Mei; Steven Colleman; Pouya Houshmand; Sebastian Karl; Marian Verhelst (IEEE Trans. Computers, 2025)