VLA→SNN Pulse Encoding for Physical AI
The missing interface layer between Vision-Language-Action models and Spiking Neural Networks.
Today's Physical AI gap: VLA models (RT-2, Octo, OpenVLA) produce high-level action tokens, but neuromorphic hardware requires spike trains. No standard exists to bridge this translation.
NeuroBridge solves this with a binary protocol achieving sub-millisecond latency while maintaining semantic fidelity across the VLA→SNN boundary.
| Component | Function |
|---|---|
| NBP Protocol | Binary VLA→SNN translation, MsgPack encoding |
| Agent Governance Layer | On-chain trust engine, EU AI Act compliance |
| Compliance Engine | Automated Art.50/Art.14 checking |
| Python SDK | Integration with existing VLA models |
| Legal-Policy Bridge | Translates regulatory requirements to code constraints |
NeuroBridge is designed for full compliance with: