11 TOPS photonic convolutional accelerator for optical neural networks

  • 1.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 2.

    Schalkoff, R. J. Pattern recognition. In Wiley Encyclopedia of Computer Science and Engineering (ed. Wah, B. W.) https://doi.org/10.1002/9780470050118.ecse302 (Wiley, 2007).

  • 3.

    Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 4.

    Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 5.

    Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).

    Article 

    Google Scholar
     

  • 6.

    Yao, P. et al. Fully hardware-implemented memristor convolutional neural network. Nature 577, 641–646 (2020).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 7.

    Lawrence, S., Giles, C. L., Tsoi, A. C. & Back, A. D. Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8, 98–113 (1997).

    CAS 
    Article 

    Google Scholar
     

  • 8.

    Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441–446 (2017).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 9.

    Larger, L. et al. High-speed photonic reservoir computing using a time-delay-based architecture: Million words per second classification. Phys. Rev. X 7, 011015 (2017).


    Google Scholar
     

  • 10.

    Peng, H.-T., Nahmias, M. A., de Lima, T. F., Tait, A. N. & Shastri, B. J. Neuromorphic photonic integrated circuits. IEEE J. Sel. Top. Quantum Electron. 24, 6101715 (2018).


    Google Scholar
     

  • 11.

    Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018).

    ADS 
    MathSciNet 
    CAS 
    Article 

    Google Scholar
     

  • 12.

    Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. P. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 13.

    Ambrogio, S. et al. Equivalent-accuracy accelerated neural-network training using analogue memory. Nature 558, 60–67 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 14.

    Esser, S. K. et al. Convolutional networks for fast, energy-efficient neuromorphic computing. Proc. Natl Acad. Sci. USA 113, 11441–11446 (2016).

    CAS 
    Article 

    Google Scholar
     

  • 15.

    Graves, A. et al. Hybrid computing using a neural network with dynamic external memory. Nature 538, 471–476 (2016).

    ADS 
    Article 

    Google Scholar
     

  • 16.

    Miller, D. A. B. Attojoule optoelectronics for low-energy information processing and communications. J. Lightwave Technol. 35, 346–396 (2017).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 17.

    Appeltant, L. et al. Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 18.

    Chang, J., Sitzmann, V., Dun, X., Heidrich, W. & Wetzstein, G. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci. Rep. 8, 12324 (2018).

    ADS 
    Article 

    Google Scholar
     

  • 19.

    Vandoorne, K. et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat. Commun. 5, 3541 (2014).

    ADS 
    Article 

    Google Scholar
     

  • 20.

    Brunner, D., Soriano, M. C., Mirasso, C. R. & Fischer, I. Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun. 4, 1364 (2013).

    ADS 
    Article 

    Google Scholar
     

  • 21.

    Tait, A. N., Chang, J., Shastri, B. J., Nahmias, M. A. & Prucnal, P. R. Demonstration of WDM weighted addition for principal component analysis. Opt. Express 23, 12758–12765 (2015).

    ADS 
    Article 

    Google Scholar
     

  • 22.

    Xu, X. et al. Photonic perceptron based on a Kerr microcomb for high‐speed, scalable, optical neural networks. Laser Photon. Rev. 14, https://doi.org/10.1002/lpor.202000070 (2020).

  • 23.

    Pasquazi, A. et al. Micro-combs: a novel generation of optical sources. Phys. Rep. 729, 1–81 (2018).

    ADS 
    MathSciNet 
    CAS 
    Article 

    Google Scholar
     

  • 24.

    Moss, D. J., Morandotti, R., Gaeta, A. L. & Lipson, M. New CMOS-compatible platforms based on silicon nitride and Hydex for nonlinear optics. Nat. Photon. 7, 597–607 (2013).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 25.

    Kippenberg, T. J., Gaeta, A. L., Lipson, M. & Gorodetsky, M. L. Dissipative Kerr solitons in optical microresonators. Science 361, eaan8083 (2018).

    Article 

    Google Scholar
     

  • 26.

    Savchenkov, A. A. et al. Tunable optical frequency comb with a crystalline whispering gallery mode resonator. Phys. Rev. Lett. 101, 093902 (2008).

    ADS 
    Article 

    Google Scholar
     

  • 27.

    Spencer, D. T. et al. An optical-frequency synthesizer using integrated photonics. Nature 557, 81–85 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 28.

    Marin-Palomo, P. et al. Microresonator-based solitons for massively parallel coherent optical communications. Nature 546, 274–279 (2017).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 29.

    Kues, M. et al. Quantum optical microcombs. Nat. Photon. 13, 170–179 (2019).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 30.

    Cole, D. C., Lamb, E. S., Del’Haye, P., Diddams, S. A. & Papp, S. B. Soliton crystals in Kerr resonators. Nat. Photon. 11, 671–676 (2017).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 31.

    Stern, B., Ji, X., Okawachi, Y., Gaeta, A. L. & Lipson, M. Battery-operated integrated frequency comb generator. Nature 562, 401–405 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 32.

    Wu, J. et al. RF photonics: an optical microcombs’ perspective. IEEE J. Sel. Top. Quant. Electron. 24, 6101020 (2018).

    ADS 

    Google Scholar
     

  • 33.

    LeCun, Y., Cortes, C. & Borges, C. J. C. The MNIST database of handwritten digits. http://yann.lecun.com/exdb/mnist/

  • 34.

    Metcalf, A. J. et al. Integrated line-by-line optical pulse shaper for high-fidelity and rapidly reconfigurable RF-filtering. Opt. Express 24, 23925–23940 (2016).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 35.

    NVIDIA Corporation. Comparison of Convolution Methods for GPUs. http://ska-sdp.org/publications/released-sdp-memos-2 (2018).

  • 36.

    Sahin, E., Ooi, K., Png, C. & Tan, D. Large, scalable dispersion engineering using cladding-modulated Bragg gratings on a silicon chip. Appl. Phys. Lett. 110, 161113 (2017).

    ADS 
    Article 

    Google Scholar
     

  • 37.

    Roeloffzen, C. G. H. et al. Low-loss Si3N4 TriPleX optical waveguides: technology and applications overview. IEEE J. Sel. Top. Quantum Electron. 24, 4400321 (2018).

    Article 

    Google Scholar
     

  • 38.

    Wang, C. et al. Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages. Nature 562, 101–104 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 39.

    Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

    ADS 
    CAS 
    Article 

    Google Scholar
     

  • 40.

    Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).

    ADS 
    CAS 
    Article 

    Google Scholar
     

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