The idea in one line

2DNeuralVision is building a first-generation programmable optical neural network (ONN) based on 2D materials that does part of the AI math with light instead of electricity. That can make computer vision quicker and more energy efficient – exactly what advanced driver assistance needs.

What’s already in place

• A first optical “crossbar” chip with tiny light paths and graphene elements that act like adjustable weights and detectors.
• A software bridge: train your model in PyTorch and the resulting weights are sent to the chip through an FPGA controller for live, optical inference.

This creates a fully functional pipeline for live optical inference inside standard machine-learning workflows.

Why it represents a step change

• Speed & efficiency: moving heavy calculations into the optical domain can cut latency and power use compared to standard processors.
• Built for the road ahead: we’re progressing through lab validation (TRL steps) and keeping an eye on automotive tests and export control rules, so future demos can move fast.

What comes next

Chip packaging and board integration, then application demos on real traffic scenes (including fog and rain), measured against today’s CMOS baselines for power, speed, and accuracy.

These results will guide the project toward higher TRL demonstrators and future exploitation pathways.