Chinese Optics Letters, 2021, 19 (8): 082501, Published Online: Apr. 20, 2021   

Optical tensor core architecture for neural network training based on dual-layer waveguide topology and homodyne detection Download: 805次

Author Affiliations
State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (iMLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Shaofu Xu, Weiwen Zou. Optical tensor core architecture for neural network training based on dual-layer waveguide topology and homodyne detection[J]. Chinese Optics Letters, 2021, 19(8): 082501.

References

[1] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 2015, 521: 436.

[2] D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, D. Hassabis. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 2018, 362: 1140.

[3] J. Shi, F. Zhang, D. Ben, S. Pan. Photonic-assisted single system for microwave frequency and phase noise measurement. Chin. Opt. Lett., 2020, 18: 092501.

[4] R. Wang, S. Xu, J. Chen, W. Zou. Ultra-wideband signal acquisition by use of channel-interleaved photonic analog-to-digital converter under the assistance of dilated fully convolutional network. Chin. Opt. Lett., 2020, 18: 123901.

[5] S. Xu, X. Zou, B. Ma, J. Chen, L. Yu, W. Zou. Deep-learning-powered photonic analog-to-digital conversion. Light: Sci. Appl., 2019, 8: 66.

[6] L. Yu, W. Zou, X. Li, J. Chen. An X- and Ku-band multifunctional radar receiver based on photonic parametric sampling. Chin. Opt. Lett., 2020, 18: 042501.

[7] AmodeiD.HernandezD., “AI and compute,” (2018).

[8] HorowitzM., “Computing’s energy problem (and what we can do about it),” in IEEE International Solid-state Circuits Conference (2014), p. 10.

[9] M. A. Nahmias, T. F. Lima, A. N. Tait, H. Peng, B. J. Shastri, P. R. Prucnal. Photonic multiply-accumulate operations for neural networks. IEEE J. Sel. Top. Quantum Electron., 2020, 26: 7701518.

[10] Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, M. Soljačić. Deep learning with coherent nanophotonic circuits. Nat. Photon., 2017, 11: 441.

[11] S. Xu, J. Wang, R. Wang, J. Chen, W. Zou. High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays. Opt. Express, 2019, 27: 19778.

[12] V. Bangari, B. A. Marquez, H. Miller, A. N. Tait, M. A. Nahmias, T. Lima, H. Peng, P. R. Prucnal, B. J. Shastri. Digital electronics and analog photonics for convolutional neural networks (DEAP-CNNs). IEEE J. Sel. Top. Quantum Electron., 2020, 26: 7701213.

[13] Y. Zuo, B. Li, Y. Zhao, Y. Jiang, Y. Chen, P. Chen, G. Jo, J. Liu, S. Du. All-optical neural network with nonlinear activation functions. Optica, 2019, 6: 1132.

[14] X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, A. Ozcan. All-optical machine learning using diffractive deep neural networks. Science, 2018, 361: 1004.

[15] R. Hamerly, L. Bernstein, A. Sludds, M. Soljačić, D. Englund. Large-scale optical neural networks based on photoelectric multiplication. Phys. Rev. X, 2019, 9: 021032.

[16] J. Chiles, S. Buckley, N. Nader, S. Nam, R. P. Mirin, J. M. Shainline. Multi-planar amorphous silicon photonics with compact interplanar couplers, cross talk mitigation, and low crossing loss. APL Photon., 2017, 2: 116101.

[17] ChetlurS.WoolleyC.VandermerschP.CohenJ.TranJ.CatanzaroB.ShelhamerE., “cuDNN: efficient primitives for deep learning,” arXiv:1410.0759 (2014).

[18] J. Chiles, S. M. Buckley, S. Nam, R. P. Mirin, J. M. Shainline. Design, fabrication, and metrology of 10 × 100 multi-planar integrated photonic routing manifolds for neural networks. APL Photon., 2018, 3: 106101.

[19] J. Lee, S. Cho, W. Choi. An equivalent circuit model for a Ge waveguide photodetector on Si. IEEE Photon. Technol. Lett., 2016, 28: 2435.

[20] P. Yao, H. Wu, B. Gao, J. Tang, Q. Zhang, W. Zhang, J. J. Yang, H. Qian. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577: 641.

Shaofu Xu, Weiwen Zou. Optical tensor core architecture for neural network training based on dual-layer waveguide topology and homodyne detection[J]. Chinese Optics Letters, 2021, 19(8): 082501.

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