18 citations · 20 across the 6 of their papers we have counts for
7 papers
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.…
Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data
Douwe den Blanken, Charlotte Frenkel
On-device learning at the edge enables low-latency, private personalization with improved long-term robustness and reduced maintenance costs. Yet, achieving scalable, low-power end…
An Event-Based Digital Compute-In-Memory Accelerator with Flexible Operand Resolution and Layer-Wise Weight/Output Stationarity
Nicolas Chauvaux, Adrian Kneip, Christoph Posch +2
Compute-in-memory (CIM) accelerators for spiking neural networks (SNNs) are promising solutions to enable s-level inference latency and ultra-low energy in edge vision applicati…