The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration by exploiting optical bandwidth and parallelism to reshape data movement and computation. This review reframes photonic computing from a circuits-and-systems perspective, moving beyond building-block progress toward cross-layer system analysis and full-stack design automation. We synthesize recent advances through a bottleneck-driven taxonomy that delineates the operating regimes and scaling trends where photonics can deliver end-to-end sustained benefits. A central theme is cross-layer co-design and workload-adaptive programmability to sustain high efficiency and versatility across evolving application domains at scale. We further argue that Electronic-Photonic Design Automation (EPDA) will be pivotal, enabling closed-loop co-optimization across simulation, inverse design, system modeling, and physical implementation. By charting a roadmap from laboratory prototypes to scalable, reproducible electronic-photonic ecosystems, this review aims to guide the CAS community toward an automated, system-centric era of photonic machine intelligence.

翻译：机器智能工作负载的指数级增长正与后摩尔时代在功耗、存储和互连方面的极限产生冲突，这促使计算基底需要超越仅仅依靠晶体管密度的扩展。集成光子学正成为人工智能加速的候选方案，通过利用光带宽和并行性来重塑数据移动和计算。本综述从电路与系统视角重新审视光子计算，超越基础模块的进展，转向跨层系统分析和全栈设计自动化。我们通过一个瓶颈驱动的分类法综合了近期进展，该分类法界定了光子学能够提供端到端持续效益的工作体制和扩展趋势。一个核心主题是跨层协同设计与工作负载自适应的可编程性，以在多样化的应用领域大规模维持高效率和多功能性。我们进一步论证，电子-光子设计自动化将至关重要，它能够实现仿真、逆向设计、系统建模和物理实现之间的闭环协同优化。通过绘制从实验室原型到可扩展、可复现的电子-光子生态系统的路线图，本综述旨在引导电路与系统社群迈向一个自动化、以系统为中心的光子机器智能时代。