In this work, we identify three considerations that are essential for realizing practical photonic AI systems at scale: (1) dynamic tensor operation support for modern models rather than only weight-static kernels, especially for attention/Transformer-style workloads; (2) systematic management of conversion, control, and data-movement overheads, where multiplexing and dataflow must amortize electronic costs instead of letting ADC/DAC and I/O dominate; and (3) robustness under hardware non-idealities that become more severe as integration density grows. To study these coupled tradeoffs quantitatively, and to ensure they remain meaningful under real implementation constraints, we build a cross-layer toolchain that supports photonic AI design from early exploration to physical realization. SimPhony provides implementation-aware modeling and rapid cross-layer evaluation, translating physical costs into system-level metrics so architectural decisions are grounded in realistic assumptions. ADEPT and ADEPT-Z enable end-to-end circuit and topology exploration, connecting system objectives to feasible photonic fabrics under practical device and circuit constraints. Finally, Apollo and LiDAR provide scalable photonic physical design automation, turning candidate circuits into manufacturable layouts while accounting for routing, thermal, and crosstalk constraints.

翻译：在本研究中，我们提出了实现大规模实用光子人工智能系统的三个关键考量因素：（1）为现代模型提供动态张量运算支持，而非仅限于权重静态核，尤其针对注意力/Transformer类工作负载；（2）系统化管理转换、控制和数据移动开销，通过复用与数据流分摊电子成本，避免ADC/DAC及I/O成为主导因素；（3）在硬件非理想性影响下保持鲁棒性，该问题随集成密度提升而日益凸显。为量化研究这些相互关联的权衡关系，并确保其在真实实现约束下保持有效性，我们构建了支持光子人工智能从早期探索到物理实现的全流程跨层工具链。SimPhony提供实现感知建模与快速跨层评估，将物理成本转化为系统级指标，使架构决策建立在现实假设基础上。ADEPT与ADEPT-Z支持端到端电路与拓扑探索，在实用器件与电路约束下将系统目标与可行光子架构相连接。最后，Apollo与LiDAR提供可扩展的光子物理设计自动化，在考虑布线、热效应和串扰约束的同时，将候选电路转化为可制造的版图设计。