Photonics for artificial intelligence and neuromorphic computing

被引:804
|
作者
Shastri, Bhavin J. [1 ,2 ]
Tait, Alexander N. [2 ,3 ]
de Lima, T. Ferreira [2 ]
Pernice, Wolfram H. P. [4 ,5 ]
Bhaskaran, Harish [6 ]
Wright, C. D. [7 ]
Prucnal, Paul R. [2 ]
机构
[1] Queens Univ, Dept Phys Engn Phys & Astron, Kingston, ON, Canada
[2] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA
[3] NIST, Appl Phys Div, Boulder, CO 80309 USA
[4] Univ Munster, Inst Phys, Munster, Germany
[5] Univ Munster, Ctr Soft Nanosci SoN, Munster, Germany
[6] Univ Oxford, Dept Mat, Oxford, England
[7] Univ Exeter, Dept Engn, Exeter, Devon, England
基金
加拿大自然科学与工程研究理事会; 英国工程与自然科学研究理事会; 美国国家科学基金会;
关键词
NEURAL-NETWORKS; LITHIUM-NIOBATE; MODULATOR; LASERS; IMPLEMENTATION; INTEGRATION; PERFORMANCE; MACHINE; MEMORY; EXCITABILITY;
D O I
10.1038/s41566-020-00754-y
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
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.
引用
收藏
页码:102 / 114
页数:13
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