Author

Charlotte Frenkel

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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

#edge computing Preprint Sep 2026

FlexSpIM: An Event-Based Digital Compute-In-Memory Accelerator with Flexible Operand Resolution and Layer-Wise Hybrid Stationarity

FlexSpIM, a digital CIM architecture supporting arbitrary operand resolution and shape within a unified storage for weights and neuron states, is introduced, enabling a layer-level hybrid weight- and output-stationary dataflow, maximizing operand reuse and reducing costly on- and off-chip data movement during SNN execu...

Nicolas Chauvaux, Adrian Kneip, Charlotte Frenkel · 0 citations