arXiv (OAI Expanded)
open access
A 25-$μ$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 $μ$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 soluti
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Minimal Solvers for Full-DoF Motion Estimation from Asynchronous Differential SfM#821905
- Toward On-Chip Training of Spiking Neural Networks for Dense Event-Based Vision#1116736
- GraphEnet: Event-driven Human Pose Estimation with a Graph Neural Network#1014555
- i-WiViG: Interpretable Window Vision GNN#158676
- The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation#1104677
Record · ID 1109282
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive