Photonic Substrate Intelligence: A Unified Architecture for Speed-of-Light Neural Network Inference, Wavelength-Multiplexed Massive Parallelism, and Holographic On-Device Training via Thermomagnetic Physical Substrates

We describe a unified speculative architecture for neural network computation in which inference, massive parallelism, and on-device training are performed primarily in the optical domain. The system employs two distinct physical substrates: a write-once DNA cassette tape providing petabyte-scale context input via optical imaging, and a rewritable sputtered magneto-optical thin-film tape storing neural network weights as magnetic domain patterns. The architecture combines spatial light modulator ternary weight layers, passive nonlinear optical activation, wavelength-division multiplexed parallel inference, holographic error computation, orbital angular momentum gradient encoding, and thermomagnetic weight writing into a unified system. The resulting form factor is a 30cm cube providing tens of thousands of concurrent frontier-class model inferences at kilowatt-scale power with no processor, no memory bus, and no required network connection. All component concepts draw upon established physical principles and prior experimental demonstrations. The novel claim is architectural combination. This document constitutes a prior art disclosure establishing date of conception as of 13 March 2026.