4 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…
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.…
Accelerated Predictive Coding Networks via Direct Kolen-Pollack Feedback Alignment
Davide Casnici, Martin Lefebvre, Justin Dauwels +1
Predictive coding (PC) is a biologically inspired algorithm for training neural networks that relies only on local updates, allowing parallel learning across layers. However, pract…