Toward brain-inspired intelligence: a review of photonic neuromorphic computing systems

Abstract Inspired by the key elements and principles in the brain, photonic neuromorphic computing shows great potential for building next-generation intelligent processing systems with high parallelism, low latency, low power consumption, and self-learning capabilities. While summarizing significant advances in this field, this review offers insights into how photonic neuromorphic computing systems support next-generation intelligent information processing. Specifically, we discuss emerging materials and devices, which support more compact integration of efficient physical architectures. Architectures such as photonic spiking neural networks and reservoir computing, together with associated learning paradigms, show a trend of collaborative development. Then we present promising applications, where neuromorphic computing systems provide broadband and multi-domain perception and processing. Finally, we discuss key challenges and future directions. This review aims to offer a clear and comprehensive overview for researchers across a broad community. We hope to present these insights in a “neuromorphic” manner, to reveal why learning from the brain has become increasingly important, especially for overcoming the efficiency bottleneck in traditional von Neumann architecture and data-intensive artificial neural networks.