Phasor’s cross-modal TCP fusion architecture across three simulation environments — sensor fusion, cinematic AV demo, and full ROS rover simulation with Doosan M1013 arm. This is what edge AI looks like at watts-scale power.
Bird’s-eye object tracking, raw event camera stream and SNN spike raster — all from the same neuromorphic fusion model running on BrainChip Akida hardware.
All four sensor fusion layers running simultaneously. The long cut includes architecture breakdown and GPU benchmark comparison.
Rover with Doosan M1013 6-DOF arm. ROS2 topic graph live. TCP confidence propagating from EVS camera through SNN fusion to arm controller — the complete closed-loop neuromorphic stack.
Three layers. One stack. The data moat enables the algorithm. The algorithm targets the hardware. The hardware enables edge deployment at watts-scale power.
Full sensor fusion dashboard — three sensor streams fusing in real time, BEV object tracking with range rings, live performance metrics and system log.
SNN inference on BrainChip Akida vs GPU equivalent. NICE 2026 & AAAI 2026 peer-reviewed.
End-to-end cross-modal fusion: EVS + LiDAR + IMU. 847Hz continuous update rate on Akida hardware.
Event-based datasets across 7 domains — the world’s largest neuromorphic training library.
Temporal Confidence Propagation — maintains object tracking under occlusion. No standard SNN framework does this.