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By migrating critical operations to the optical domain, photonic neural networks (PNNs) offer a promising solution to significantly reduce system latency and energy consumption. Driven by silicon photonics, on-chip PNNs achieve high-density integration on scalable platforms, promoting the evolution of optoelectronic integration systems. This paper presents a comprehensive review of emerging integrated PNN technology for AI, employing a cross-layer framework that spans from fundamental physical devices to system-level implementation. Based on the requirements of PNNs, this review analyzes key hardware devices and mainstream computing core architectures, evaluates integration strategies and discusses advanced system packaging. 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