Democratizing Electronic-Photonic AI Systems: An Open-Source AI-Infused Cross-Layer Co-Design and Design Automation Toolflow

2025-12-31

AI Summary

This work proposes the first open-source electronic-photonic co-design automation (EPDA) framework to address the high cross-layer complexity, immature toolchains, prolonged development cycles, and interdisciplinary collaboration challenges inherent in electronic-photonic AI system design. The framework integrates AI-driven physical modeling and system-level optimization, featuring process-aware inverse design, an AI-enhanced Maxwell solver, and a scalable training algorithm for meta-optical neural networks. Built upon SimPhony, it enables an end-to-end toolflow that supports efficient design space exploration and rapid deployment of Transformer-like photonic AI architectures. By significantly lowering the barrier to entry, this framework accelerates the adoption and innovation of intelligent electronic-photonic systems.

Technology Category

• Philosophy and Ethics of AI: Philosophical Foundations of AI
• Computer Vision: Computational Photography, Image & Video Synthesis
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• electronic-photonic AI systems
• design automation
• cross-layer co-design
• EPDA
• democratization

Innovation

• photonic AI
• cross-layer co-design
• design automation
• inverse design
• open-source framework