Advanced Photonics, Volume. 4, Issue 4, 044001(2022)

Silicon-based optoelectronics for general-purpose matrix computation: a review

Pengfei Xu1 and Zhiping Zhou1,2、*
Author Affiliations
  • 1Peking University, State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics, Beijing, China
  • 2Chinese Academy of Sciences, Shanghai Institute of Optics and Fine Mechanics, Shanghai, China
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    References(97)

    [1] Z. Zhou. Silicon Based Optoelectronics(2021).

    [13] K. Simonyan, A. Zisserman. Very deep convolutional networks for large scale image recognition(2015).

    [14] K. He et al. Deep residual learning for image recognition(2016).

    [22] G. M. Amdahl. Validity of the single processor approach to achieving large scale computing capabilities. AFIPS Conf. Proc., 483-485(1967).

    [27] N. G. Karthikeyan et al. Mobile Artificial Intelligence Projects(2019).

    [38] N. C. Harris et al. Accelerating artificial intelligence with silicon photonics, 1-4(2020).

    [63] R. Hamerly et al. Large scale optical neural networks based on photoelectric multiplication. Phys. Rev. X, 9, 021032(2019).

    [95] S. Foucart, H. Rauhut. A Mathematical Introduction to Compressive Sensing(2013).

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    Pengfei Xu, Zhiping Zhou, "Silicon-based optoelectronics for general-purpose matrix computation: a review," Adv. Photon. 4, 044001 (2022)

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    Paper Information

    Category: Reviews

    Received: Apr. 24, 2022

    Accepted: Jun. 10, 2022

    Published Online: Jul. 7, 2022

    The Author Email: Zhiping Zhou (zjzhou@pku.edu.cn)

    DOI:10.1117/1.AP.4.4.044001

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