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Domain-specific neural network accelerators have seen growing interest in recent years due to their improved energy efficiency and inference performance compared to CPUs and GPUs. In this chapter, we propose a novel cross-layer optimized neural network accelerator called CrossLight that leverages silicon photonics. CrossLight employs device-level engineering for resilience to process variations and thermal crosstalk, circuit-level tuning enhancements for inference latency reduction, and architecture-level optimization to enable higher resolution, better energy-efficiency, and improved throughput. On average, CrossLight offers 9.5× lower energy-per-bit and 15.9× higher performance-per-watt at 16-bit resolution than state-of-the-art photonic deep learning accelerators.