一句话：提出首个面向衍射光学神经网络的端到端光学机器学习编译框架，集成可微物理内核、计算加速与领域特定语言。
为降低衍射光学神经网络（DONN）设计、探索与部署的门槛，本文提出 LightRidge——首个端到端光学机器学习编译框架。该框架包含三部分：精确且可微的光学物理内核，以支持 DONN 架构的完整探索；光学物理计算内核加速，显著降低训练、仿真与部署的运行时开销；以及灵活的光学系统建模与用户友好的领域特定语言（DSL）。基于真实物理光学系统的实验表明：物理内核与低层物理和系统精确对应；物理感知仿真负载相比最先进商用系统获得显著加速；硬件原型与片上集成案例验证了有效的架构设计空间探索；并成功在 DONN 架构与拓扑上演示了图像分类和图像分割任务，提炼出新的 DONN 设计原则。
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.
@inproceedings{paperbot10120,
title = {LightRidge: An End-to-end Agile Design Framework for Diffractive Optical Neural Networks},
author = {Yingjie Li and Ruiyang Chen and Minhan Lou and Berardi Sensale-Rodriguez and Weilu Gao and Cunxi Yu},
booktitle = {ASPLOS},
year = {2023},
doi = {10.1145/3623278.3624757}
}