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

TL;DR

LightRidge is an innovative end-to-end framework that simplifies the design, exploration, and deployment of diffractive optical neural networks by integrating precise physics modeling, acceleration, and flexible system design, enabling practical applications.

Contribution

It introduces the first comprehensive optical ML compilation framework that combines physics kernels, acceleration, and DSL for efficient DONN development and deployment.

Findings

Precise correlation of physics kernels with low-level optical systems.

Significant runtime speedups in physics-aware emulation.

Successful demonstrations of advanced image tasks with DONNs.

Abstract

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