开源工具链让光电子AI系统设计更易上手，打破跨层技术壁垒。
Democratizing Electronic-Photonic AI Systems: An Open-Source AI-Infused Cross-Layer Co-Design and Design Automation Toolflow
- 构建跨层协同设计框架，贯通器件到算法全链条。
- 推出SimPhony工具，支持快速评估与设计空间探索。
- 融合AI加速光子芯片逆向设计，适合芯片与算法协同研发者。
光子学正成为高性能AI系统与科学计算的核心技术，具备无与伦比的运算速度、并行能力与能效优势。然而，光电子AI系统的开发与部署仍面临巨大挑战，涉及器件物理、电路设计、系统架构和AI算法等多个层级，且缺乏成熟的电子-光子设计自动化（EPDA）工具链，导致设计周期长、效率低，制约跨学科创新与协同演进。本文提出一种跨层协同设计与自动化框架，旨在推动光电子AI系统开发的民主化。首先介绍可扩展的光子边缘AI与Transformer推理架构；随后推出SimPhony——一个开源建模工具，支持快速评估与设计空间探索。进一步实现基于AI的光子设计自动化突破：包括物理驱动的AI Maxwell求解器、面向制造的逆向设计框架，以及用于元光学神经网络的可扩展逆向训练算法，共同构成下一代光电子AI系统的可扩展EPDA栈。
原文摘要 · Abstract (English)
Photonics is becoming a cornerstone technology for high-performance AI systems and scientific computing, offering unparalleled speed, parallelism, and energy efficiency. Despite this promise, the design and deployment of electronic-photonic AI systems remain highly challenging due to a steep learning curve across multiple layers, spanning device physics, circuit design, system architecture, and AI algorithms. The absence of a mature electronic-photonic design automation (EPDA) toolchain leads to long, inefficient design cycles and limits cross-disciplinary innovation and co-evolution. In this work, we present a cross-layer co-design and automation framework aimed at democratizing photonic AI system development. We begin by introducing our architecture designs for scalable photonic edge AI and Transformer inference, followed by SimPhony, an open-source modeling tool for rapid EPIC AI system evaluation and design-space exploration. We then highlight advances in AI-enabled photonic design automation, including physical AI-based Maxwell solvers, a fabrication-aware inverse design framework, and a scalable inverse training algorithm for meta-optical neural networks, enabling a scalable EPDA stack for next-generation electronic-photonic AI systems.
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