Light Is Replacing Silicon in AI Computing

GPUs are hitting a physical wall. Trillion-parameter models drain gigawatts and melt racks. The fix isn't more transistors — it's photons.

How it works:
Deep learning is mostly matrix multiplication. In a photonic tensor core, laser beams pass through a mesh of Mach-Zehnder Interferometers. By modulating light's phase and intensity, the matrix multiplication happens as the light passes through — with near-zero heat.

The gains:

Sub-nanosecond latency (light travels ~80,000 km/s in silicon waveguides)

Wavelength Division Multiplexing — multiple calculations run simultaneously on different colors of light through one waveguide

Drastically lower cooling costs

Where it stands: Pure optical computers are still in development, but hybrid electro-photonic chips are already in specialized cloud facilities. Existing PyTorch and TensorFlow code runs via compiler layers that translate computation graphs into optical phase configs.

Full read: https://www.nizover.com/en/blog/tech-optical-neural-networks-photonic-ai