Abstract:Edge intelligence is constrained by the energy and latency costs of shuttling data through electronic memory hierarchies. Optical systems offer a fundamentally different computational regime: once an input wavefront is launched into a structured medium, propagation, diffraction, and interference jointly enact a linear transformation whose cost is determined by wave physics rather than by clocked arithmetic. This paper develops a rigorous systems-level treatment of that regime and introduces a hybrid diffractive holographic architecture for image classification. The proposed model couples a Diffractive Optical Neural Network (DONN) with a Holographic Interference-Based Learning (HIBL) operator a formal map from digitally optimized phase distributions to physically realizable, fabrication-compatible interference patterns embeddable in passive optical elements. We express the full inference pipeline as a composition of encoding, phase modulation, free-space propagation, and intensity measurement operators, making explicit which quantities are learned, which are fixed by design, and where nonlinearity enters through photodetection. This operator-theoretic view resolves a persistent gap in the optical-ML literature between learning a transformation and physically realizing it. In physics-informed simulation on MNIST, a three-layer system with approximately 25,000 phase elements achieves 91.2% test accuracy with propagation-limited nanosecond-scale latency. The primary contribution is not a performance claim but a precise computational framework: learned representations can be physically embedded into structured optical media so that inference is executed by wavefront transformation through a passive, fabricated object rather than by sequential electronic multiply accumulate operations.
| Comments: | 18 pages, 3 figures. Operator-theoretic formulation and simulation of a hybrid diffractive-holographic optical neural system |
| Subjects: | Hardware Architecture (cs.AR); Machine Learning (cs.LG) |
| ACM classes: | I.2.6; I.4.9; B.7.2 |
| Cite as: | arXiv:2604.15364 [cs.AR] |
| (or arXiv:2604.15364v1 [cs.AR] for this version) | |
| https://doi.org/10.48550/arXiv.2604.15364 arXiv-issued DOI via DataCite (pending registration) |
From: Prakul Hiremath [view email]
[v1]
Tue, 14 Apr 2026 10:34:14 UTC (467 KB)
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