Marian Verhelst

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Papers (12)

1. Hardware Generation and Exploration of Lookup Table-Based Accelerators for 1.58-bit LLM Inference cs.AR · 2026 · author #4
2. The Hyperscale Lottery: How State-Space Models Have Sacrificed Edge Efficiency cs.AR · 2026 · author #3
3. P3-LLM: An Integrated NPU-PIM Accelerator for Edge LLM Inference Using Hybrid Numerical Formats cs.AR · 2025 · author #6
4. Hardware-Centric Analysis of DeepSeek's Multi-Head Latent Attention cs.AR · 2025 · author #2
5. Fine-Grained Fusion: The Missing Piece in Area-Efficient State Space Model Acceleration cs.AR · 2025 · author #3
6. A multi-layered energy consumption model for smart wireless acoustic sensor networks eess.AS · 2018 · author #8
7. BinarEye: An Always-On Energy-Accuracy-Scalable Binary CNN Processor With All Memory On Chip in 28nm CMOS cs.DC · 2018 · author #5
8. Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion cs.CV · 2018 · author #3
9. Minimum Energy Quantized Neural Networks cs.NE · 2017 · author #4
10. A 0.3-2.6 TOPS/W Precision-Scalable Processor for Real-Time Large-Scale ConvNets cs.AR · 2016 · author #2
11. Energy-Efficient ConvNets Through Approximate Computing cs.CV · 2016 · author #4
12. Understanding interdependency through complex information sharing cs.IT · 2015 · author #5

Mentions

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Frequent Coauthors

• Bert Moons 5 shared papers
• Robin Geens 4 shared papers
• Fernando Rosas 2 shared papers
• Sofie Pollin 2 shared papers
• Thierry Tambe 2 shared papers
• Arne Symons 1 shared papers
• Bart Vanrumste 1 shared papers
• Bert De Brabandere 1 shared papers
• Boris Murmann 1 shared papers
• Chao Fang 1 shared papers
• Christopher J. Ellison 1 shared papers
• Daniel Bankman 1 shared papers
• Gert Dekkers 1 shared papers
• Jonas De Schouwer 1 shared papers
• Joran Heldens 1 shared papers
• Joren Dumoulin 1 shared papers
• Koen Goetschalckx 1 shared papers
• Lita Yang 1 shared papers
• Luc Van Gool 1 shared papers
• Matthijs Van keirsbilck 1 shared papers