Integrated Neuromorphic Photonic Computing for AI Acceleration: Emerging Devices, Network Architectures, and Future Paradigms.

Abstract

Deep learning stands as a cornerstone of modern artificial intelligence (AI), revolutionizing fields from computer vision to large language models (LLMs). However, as electronic hardware approaches fundamental physical limits-constrained by transistor scaling challenges, von Neuman architecture, and thermal dissipation-critical bottlenecks emerge in computational density and energy efficiency. To bridge the gap between algorithmic ambition and hardware limitations, photonic neuromorphic computing emerges as a transformative candidate, exploiting light's inherent parallelism, sub-nanosecond latency, and near-zero thermal losses to natively execute matrix operations-the computational backbone of neural networks. Photonic neural networks (PNNs) have achieved influential milestones in AI acceleration, demonstrating single-chip integration of both inference and in situ training-a leap forward with profound implications for next-generation computing. This review synthesizes a decade of progress in PNNs core components, critically analyzing advances in linear synaptic devices, nonlinear neuron devices, and network architectures, summarizing their respective strengths and persistent challenges. Furthermore, application-specific requirements are systematically analyzed for PNN deployment across computational regimes: cloud-scale and edge/client-side AIs. Finally, actionable pathways are outlined for overcoming material- and system-level barriers, emphasizing topology-optimized active/passive devices and advanced packaging strategies. These multidisciplinary advances position PNNs as a paradigm-shifting platform for post-Moore AI hardware.