论文标题
人工智能和神经形态计算的光子学
Photonics for artificial intelligence and neuromorphic computing
论文作者
论文摘要
光子计算中的研究由于光子积分平台上光电组件的扩散而蓬勃发展。光子综合电路已启用了超快的人工神经网络，为新的信息处理机提供了一个框架。在此类硬件上运行的算法有可能在医学诊断，电信以及高性能和科学计算等领域解决对机器学习和人工智能的不断增长的需求。同时，神经态电子的发展突出了该领域的挑战，尤其是与处理器潜伏期有关的挑战。神经形态光子学提供了次纳秒延迟，提供了扩展人工智能领域的补充机会。在这里，我们回顾了综合光子神经形态系统的最新进展，讨论当前和未来的挑战，并概述了应对这些挑战所需的科学和技术进步。
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.
