FET: Small: LightRidge: End-to-end Agile Design for Diffractive Optical Neural Networks

最近，人们越来越多地努力推进新兴技术，这些技术在功率效率，计算效率和可持续性方面为机器学习（ML）带来了显着优势。由于在能源效率方面的巨大优势，人们对将光学计算应用于医疗传感、安全筛查、药物检测和自动驾驶等应用有着浓厚的兴趣。具体而言，光学计算在功率效率和极高的计算速度方面具有独特的优势，与ML任务的数字计算系统相比，性能得到了显着提高。该项目旨在开发一个端到端的设计基础设施，以推进ML的光学计算，涵盖从低级物理到算法到全栈系统设计。这将对从物理学到计算机科学再到机器学习的跨学科研究和现实应用领域产生更广泛的影响。该项目将产生一个开源设计基础设施LightRidge和会议教程，以促进多学科社区中的技术转让和富有成效的产学互动。该项目旨在开发一个开源的端到端设计基础设施LightRidge，以探索和推进衍射深度神经网络（DONN）在现实世界的ML任务中的应用。DNN利用自由空间光衍射形成一个光学前馈网络，就像传统的DNN架构一样，它可以在每一层中托管数百万个神经元，这些神经元与相邻层中的神经元互连，从而提供比通用处理器和特定领域加速器高出几个数量级的能效。然而，在DONN的设计、训练、探索和硬件部署方面存在几个关键的技术障碍。因此，该项目将产生一个敏捷的端到端设计和制造编程框架LightRidge，由特定领域的高性能计算开发提供支持的精确，多功能和可区分的光学物理内核组成，具有新颖的物理感知硬件-软件协同设计方法，以加强算法建模和物理硬件之间的相关性。该项目还将开发一个智能高效的设计空间探索（DSE）引擎LightRidge-DSE，以实现架构和制造参数探索，单片片上DONN集成，并演示真实世界的全光学ML任务。最后，LightRidge将作为一个开源硬件项目全面发布，该项目将为物理学、电气工程、计算机科学等多学科研究领域做出贡献，并可作为一个新的教育平台。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Recently, there have been increasing efforts to advance emerging technologies, which bring significant advantages for machine learning (ML) in terms of power efficiency, computational efficiency, and sustainability. With the considerable benefits in energy efficiency, there are significant interests in leveraging optical computing into applications, such as medical sensing, security screening, drug detection, and autonomous driving. Specifically, optical computing offers unique advantages in power efficiency and extreme computation speed, leading to significant performance improvements compared to digital computing systems for ML tasks. This project aims to develop an end-to-end design infrastructure to advance optical computing for ML, covering from low-level physics to algorithms to full-stack system design. This will generate broader impacts in cross-disciplinary research and real-world application fields from physics to computer science to ML. This project will produce an open-source design infrastructure, LightRidge, and conference tutorials to facilitate technology transfers and fruitful industry-academia interactions in a multidisciplinary community.This project aims to develop an open-source, end-to-end design infrastructure, LightRidge, to explore and advance Diffractive Deep Neural Networks (DONNs) in real-world ML tasks. DONNs utilize the free-space light diffraction to form an optical feed-forward network like conventional DNNs architecture, which can host millions of neurons in each layer that are interconnected with those in neighboring layers, offering orders of magnitude energy efficiency improvements over general-purpose processor and domain-specific accelerators. However, there are several critical technical barriers in the design, training, exploration, and hardware deployment of DONNs. Thus, this project will produce an agile end-to-end design and fabrication programming framework LightRidge, consisting of precise, versatile, and differentiable optical physics kernels powered by domain-specific high-performance-computing developments, with novel physics-aware hardware-software codesign methodologies to strengthen the correlations between algorithm modeling and physical hardware. This project will also develop an intelligent and efficient design space exploration (DSE) engine LightRidge-DSE, to enable architectural and fabrication parameters exploration, monolithic on-chip DONNs integration, and demonstrate real-world all-optical ML tasks. Finally, LightRidge will be fully released as an open-source hardware project, which will contribute to multidisciplinary research domains such as physics, electrical engineering, computer science, and can be used as a new education platform.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.