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To lower the barrier to diffractive optical neural networks (DONNs) design,exploration, and deployment, we propose LightRidge, the first end-to-endoptical ML compilation framework, which consists of (1) precise anddifferentiable optical physics kernels that enable complete explorations ofDONNs architectures, (2) optical physics computation kernel acceleration thatsignificantly reduces the runtime cost in training, emulation, and deploymentof DONNs, and (3) versatile and flexible optical system modeling anduser-friendly domain-specific-language (DSL). As a result, LightRidge frameworkenables efficient end-to-end design and deployment of DONNs, and significantlyreduces the efforts for programming, hardware-software codesign, and chipintegration. Our results are experimentally conducted with physical opticalsystems, where we demonstrate: (1) the optical physics kernels preciselycorrelated to low-level physics and systems, (2) significant speedups inruntime with physics-aware emulation workloads compared to the state-of-the-artcommercial system, (3) effective architectural design space explorationverified by the hardware prototype and on-chip integration case study, and (4)novel DONN design principles including successful demonstrations of advancedimage classification and image segmentation task using DONNs architecture andtopology.

LightRidge: An End-to-end Agile Design Framework for Diffractive Optical Neural Networks