Review demonstrates photonic neuromorphic architectures for high-speed, low-power information processing, highlighting solutions to von Neumann efficiency bottlenecks.
Key Points
To synthesize advances in photonic neuromorphic computing and examine how brain-inspired physical architectures overcome conventional computing bottlenecks.
Reviewed emerging optical materials and device technologies that enable compact physical integration.
Analyzed architectures including photonic spiking neural networks and reservoir computing alongside corresponding learning paradigms.
Evaluated applications across broadband, multi-domain processing and assessed key technological challenges.
Identified that photonic neuromorphic platforms achieve ultra-high parallelism, minimal latency, and low power consumption relative to standard electronic systems.
Demonstrated that integrating photonic spiking neural networks with specialized optical materials enables high-bandwidth, multi-domain perception capabilities.