Research in photonic computing has flourished due to the proliferation of
optoelectronic components on photonic integration platforms. Photonic
integrated circuits have enabled ultrafast artificial neural networks,
providing a framework for a new class of information processing machines.
Algorithms running on such hardware have the potential to address the growing
demand for machine learning and artificial intelligence, in areas such as
medical diagnosis, telecommunications, and high-performance and scientific
computing. In parallel, the development of neuromorphic electronics has
highlighted challenges in that domain, in particular, related to processor
latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a
complementary opportunity to extend the domain of artificial intelligence.
Here, we review recent advances in integrated photonic neuromorphic systems,
discuss current and future challenges, and outline the advances in science and
technology needed to meet those challenges.

该论文回顾了集成光子神经形态系统的最新进展，分析当前和未来的挑战，概述了克服这些挑战所需的科学和技术进步，包括光子计算、光子集成电路、人工神经网络、机器学习和神经形态光电子学。