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, particularly 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.

由于光子集成平台上光电组件的激增，光子计算的研究蓬勃发展。光子集成电路使超快速人工神经网络成为可能，为新型信息处理机器提供了框架。在这种硬件上运行的算法有潜力满足医疗诊断，电信以及高性能和科学计算等领域对机器学习和人工智能不断增长的需求。同时，神经形态电子学的发展突显了该领域的挑战，特别是与处理器延迟有关的挑战。神经形态光子学提供亚纳秒级的潜伏时间，为扩展人工智能领域提供了补充机会。这里，