HYBRID PHOTONIC COMPUTING FOR ARTIFICIAL INTELLIGENCE From Nanophotonics Physics to System Level Integration

This document presents a rigorous, physics-grounded framework for Hybrid Photonic Computing in AI, shifting the narrative from academic illusions to realistic engineering. It debunks myths of infinite energy savings, proving that after accounting for converters, lasers, and cooling, realistic efficiency gains are 5x to 10x, with a 200x latency improvement for linear operations. Grounded in Maxwell’s equations, it addresses constraints like Two-Photon Absorption and thermal crosstalk. The solution is a hybrid architecture: photonics handles linear matrix multiplications, while electronics manage non-linear activations. Innovations like Phase Change Materials enable zero-static-power in-memory computing, breaking the Von Neumann bottleneck. Crucially, the work advocates for hardware-software co-design, introducing photonics-aware training algorithms that inject hardware noise to build resilience. Finally, it identifies photonic packaging as the true industrial bottleneck, offering a pragmatic roadmap toward commercially viable, heterogeneous, and physics-aware computing systems.