18 citations · 20 across the 6 of their papers we have counts for
4 papers · 1 filter
A 25-s/inf Event-driven Graph Neural Network Processor with Spatiotemporal Caching and Spline Convolution for Ultra-low-latency AI at the Edge
Adrian Kneip, Martin Lefebvre, Daniel Gehrig +4
Dynamic-vision-sensor (DVS) cameras generate events on a per-pixel basis with a s-level temporal resolution, calling for new algorithm-hardware co-design approaches compared to…
FlexSpIM: An Event-Based Digital Compute-In-Memory Accelerator with Flexible Operand Resolution and Layer-Wise Hybrid Stationarity
Nicolas Chauvaux, Adrian Kneip, Charlotte Frenkel
Compute-in-memory (CIM) accelerators for spiking neural networks (SNNs) offer a promising solution for achieving s-level inference latency and ultra-low energy in edge vision ap…
ETHEREAL: A 25.6-s/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge
Adrian Kneip, Martin Lefebvre, Daniel Gehrig +4
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 s-level time resolution.…
Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning
Douwe den Blanken, Martin Lefebvre, Charlotte Frenkel
With the ever-increasing pervasiveness of smart edge devices, the demand is growing for applications that can be tailored to users (e.g., custom keyword spotting) or patients (e.g.…