Author

Marian Verhelst

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Preprint Sep 2026

A 25-$\mu$s/inf Event-driven Graph Neural Network Processor with Spatiotemporal Caching and Spline Convolution for Ultra-low-latency AI at the Edge

Dynamic-vision-sensor (DVS) cameras generate events on a per-pixel basis with a $\mu$s-level temporal resolution, calling for new algorithm-hardware co-design approaches compared to standard frame-based vision. While event-driven graph neural networks (EV-GNNs) emerge as a promising algorithmic solution, they raise new...

Adrian Kneip, Martin Lefebvre, Daniel Gehrig et al. · 0 citations

Preprint Aug 2026

ETHEREAL: A 25.6-$\mu$s/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge

Dynamic vision sensors (DVS) are enticing candidates to reach the low-latency, sub-ms target of edge-vision applications, as they generate events with a $\mu$s-level time resolution. However, using DVS front ends also calls for novel algorithm/hardware back ends capable of efficiently handling streams of sparse spatiot...

Adrian Kneip, Martin Lefebvre, Daniel Gehrig et al. · 0 citations

#artificial intelligence Jun 2026

Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads

This work presents the first systems characterization of agent memory, introducing a system-oriented taxonomy classifying agent memory systems along four axes, and derives 10 system recommendations covering construction scheduling, capability floors, amortization via query volume, freshness-latency tradeoffs, and fleet...

Yasmine Omri, Ziyun Gan, Zachary Broveak et al. · arXiv.org · 7 citations