Photonics for artificial intelligence and neuromorphic computing

被引:995
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
相关论文
共 136 条
  • [1] Equivalent-accuracy accelerated neural-network training using analogue memory
    Ambrogio, Stefano
    Narayanan, Pritish
    Tsai, Hsinyu
    Shelby, Robert M.
    Boybat, Irem
    di Nolfo, Carmelo
    Sidler, Severin
    Giordano, Massimo
    Bodini, Martina
    Farinha, Nathan C. P.
    Killeen, Benjamin
    Cheng, Christina
    Jaoudi, Yassine
    Burr, Geoffrey W.
    [J]. NATURE, 2018, 558 (7708) : 60 - +
  • [2] ITO-based electro-absorption modulator for photonic neural activation function
    Amin, R.
    George, J. K.
    Sun, S.
    de Lima, T. Ferreira
    Tait, A. N.
    Khurgin, J. B.
    Miscuglio, M.
    Shastri, B. J.
    Prucnal, P. R.
    El-Ghazawi, T.
    Sorger, V. J.
    [J]. APL MATERIALS, 2019, 7 (08)
  • [3] Human action recognition with a large-scale brain-inspired photonic computer
    Antonik, Piotr
    Marsal, Nicolas
    Brunner, Daniel
    Rontani, Damien
    [J]. NATURE MACHINE INTELLIGENCE, 2019, 1 (11) : 530 - 537
  • [4] Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)
    Bangari, Viraj
    Marquez, Bicky A.
    Miller, Heidi B.
    Tait, Alexander N.
    Nahmias, Mitchell A.
    de Lima, Thomas Ferreira
    Peng, Hsuan-Tung
    Prucnal, Paul R.
    Shastri, Bhavin J.
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, 2020, 26 (01)
  • [5] Roadmap on emerging hardware and technology for machine learning
    Berggren, Karl
    Xia, Qiangfei
    Likharev, Konstantin K.
    Strukov, Dmitri B.
    Jiang, Hao
    Mikolajick, Thomas
    Querlioz, Damien
    Salinga, Martin
    Erickson, John R.
    Pi, Shuang
    Xiong, Feng
    Lin, Peng
    Li, Can
    Chen, Yu
    Xiong, Shisheng
    Hoskins, Brian D.
    Daniels, Matthew W.
    Madhavan, Advait
    Liddle, James A.
    McClelland, Jabez J.
    Yang, Yuchao
    Rupp, Jennifer
    Nonnenmann, Stephen S.
    Cheng, Kwang-Ting
    Gong, Nanbo
    Lastras-Montano, Miguel Angel
    Talin, A. Alec
    Salleo, Alberto
    Shastri, Bhavin J.
    de Lima, Thomas Ferreira
    Prucnal, Paul
    Tait, Alexander N.
    Shen, Yichen
    Meng, Huaiyu
    Roques-Carmes, Charles
    Cheng, Zengguang
    Bhaskaran, Harish
    Jariwala, Deep
    Wang, Han
    Shainline, Jeffrey M.
    Segall, Kenneth
    Yang, J. Joshua
    Roy, Kaushik
    Datta, Suman
    Raychowdhury, Arijit
    [J]. NANOTECHNOLOGY, 2021, 32 (01)
  • [6] Excitability in optical systems close to Z2-symmetry
    Beri, Stefano
    Mashall, Lilia
    Gelens, Lendert
    Van der Sande, Guy
    Mezosi, Gabor
    Sorel, Marc
    Danckaert, Jan
    Verschaffelt, Guy
    [J]. PHYSICS LETTERS A, 2010, 374 (05) : 739 - 743
  • [7] Hybrid integration of silicon photonics circuits and InP lasers by photonic wire bonding
    Billah, Muhammad Rodlin
    Blaicher, Matthias
    Hoose, Tobias
    Dietrich, Philipp-Immanuel
    Marin-Palomo, Pablo
    Lindenmann, Nicole
    Nesic, Aleksandar
    Hofmann, Andreas
    Troppenz, Ute
    Moehrle, Martin
    Randel, Sebastian
    Freude, Wolfgang
    Koos, Christian
    [J]. OPTICA, 2018, 5 (07): : 876 - 883
  • [8] Programmable Photonics: An Opportunity for an Accessible Large-Volume PIC Ecosystem
    Bogaerts, Wim
    Rahim, Abdul
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS, 2020, 26 (05)
  • [9] Silicon Photonics Circuit Design: Methods, Tools and Challenges
    Bogaerts, Wim
    Chrostowski, Lukas
    [J]. LASER & PHOTONICS REVIEWS, 2018, 12 (04)
  • [10] Silicon microring resonators
    Bogaerts, Wim
    De Heyn, Peter
    Van Vaerenbergh, Thomas
    De Vos, Katrien
    Selvaraja, Shankar Kumar
    Claes, Tom
    Dumon, Pieter
    Bienstman, Peter
    Van Thourhout, Dries
    Baets, Roel
    [J]. LASER & PHOTONICS REVIEWS, 2012, 6 (01) : 47 - 73