A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M. Bethge, DeepLabCut: Markerless Pose Estimation of User-Defined Body Parts with Deep Learning (Nature, 2018).

P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science 345, 668–673 (2014).

[Crossref]

P. Minzioni, C. Lacava, T. Tanabe, J. Dong, X. Hu, G. Csaba, W. Porod, G. Singh, A. E. Willner, and A. Almaiman, “Roadmap on all-optical processing,” J. Opt. 21, 063001 (2019).

[Crossref]

P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science 345, 668–673 (2014).

[Crossref]

B. Marr, B. Degnan, P. Hasler, and D. Anderson, “Scaling energy per operation via an asynchronous pipeline,” IEEE Trans. Very Large Scale Integr. Syst. 21, 147–151 (2012).

[Crossref]

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26, 7600108 (2019).

[Crossref]

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot, “Mastering the game of go with deep neural networks and tree search,” Nature 529, 484–489 (2016).

[Crossref]

P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science 345, 668–673 (2014).

[Crossref]

G. K. Shirmanesh, R. Sokhoyan, P. C. Wu, and H. A. Atwater, “Electro-optically tunable universal metasurfaces,” arXiv:1910.02069 (2019).

A. S. Backer, “Computational inverse design for cascaded systems of metasurface optics,” arXiv:1906.10753 (2019).

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, and D. Englund, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

Q. Zhang, H. Yu, M. Barbiero, B. Wang, and M. Gu, “Artificial neural networks enabled by nanophotonics,” Light Sci. Appl. 8, 1 (2019).

[Crossref]

L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification,” Phys. Rev. X 7, 011015 (2017).

[Crossref]

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

[Crossref]

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).

[Crossref]

C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” arXiv:1705.09792 (2017).

R. Hamerly, L. Bernstein, A. Sludds, M. Soljačić, and D. Englund, “Large-scale optical neural networks based on photoelectric multiplication,” Phys. Rev. X 9, 021032 (2019).

[Crossref]

A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M. Bethge, DeepLabCut: Markerless Pose Estimation of User-Defined Body Parts with Deep Learning (Nature, 2018).

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

[Crossref]

M. Hermans, M. Burm, T. Van Vaerenbergh, J. Dambre, and P. Bienstman, “Trainable hardware for dynamical computing using error backpropagation through physical media,” Nat. Commun. 6, 6729 (2015).

[Crossref]

M. Hermans, J. Dambre, and P. Bienstman, “Optoelectronic systems trained with backpropagation through time,” IEEE Trans. Neural Netw. Learn. Syst. 26, 1545–1550 (2014).

[Crossref]

C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” arXiv:1705.09792 (2017).

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).

[Crossref]

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26, 7600108 (2019).

[Crossref]

J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner, “Reinforcement learning in a large-scale photonic recurrent neural network,” Optica 5, 756–760 (2018).

[Crossref]

G. Van der Sande, D. Brunner, and M. C. Soriano, “Advances in photonic reservoir computing,” Nanophotonics 6, 561–576 (2017).

[Crossref]

J. M. Shainline, S. M. Buckley, R. P. Mirin, and S. W. Nam, “Superconducting optoelectronic circuits for neuromorphic computing,” Phys. Rev. Appl. 7, 034013 (2017).

[Crossref]

M. Hermans, M. Burm, T. Van Vaerenbergh, J. Dambre, and P. Bienstman, “Trainable hardware for dynamical computing using error backpropagation through physical media,” Nat. Commun. 6, 6729 (2015).

[Crossref]

P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science 345, 668–673 (2014).

[Crossref]

I. Chakraborty, G. Saha, and K. Roy, “Photonic in-memory computing primitive for spiking neural networks using phase-change materials,” Phys. Rev. Appl. 11, 014063 (2019).

[Crossref]

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

[Crossref]

J. Chang and G. Wetzstein, “Deep optics for monocular depth estimation and 3D object detection,” arXiv:1904.08601 (2019).

L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification,” Phys. Rev. X 7, 011015 (2017).

[Crossref]

H. Chen, S. Jayasuriya, J. Yang, J. Stephen, S. Sivaramakrishnan, A. Veeraraghavan, and A. Molnar, “ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 903–912.

Y. Zuo, B. Li, Y. Zhao, Y. Jiang, Y.-C. Chen, P. Chen, G.-B. Jo, J. Liu, and S. Du, “All optical neural network with nonlinear activation functions,” Optica 6, 1132–1137 (2019).

Y. Zuo, B. Li, Y. Zhao, Y. Jiang, Y.-C. Chen, P. Chen, G.-B. Jo, J. Liu, and S. Du, “All optical neural network with nonlinear activation functions,” Optica 6, 1132–1137 (2019).

A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med. 25, 24–29 (2019).

[Crossref]

A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med. 25, 24–29 (2019).

[Crossref]

P. Minzioni, C. Lacava, T. Tanabe, J. Dong, X. Hu, G. Csaba, W. Porod, G. Singh, A. E. Willner, and A. Almaiman, “Roadmap on all-optical processing,” J. Opt. 21, 063001 (2019).

[Crossref]

A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med. 25, 24–29 (2019).

[Crossref]

A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M. Bethge, DeepLabCut: Markerless Pose Estimation of User-Defined Body Parts with Deep Learning (Nature, 2018).

G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag. 29, 82–97 (2012).

[Crossref]

T. Yan, J. Wu, T. Zhou, H. Xie, F. Xu, J. Fan, L. Fang, X. Lin, and Q. Dai, “Fourier-space diffractive deep neural network,” Phys. Rev. Lett. 123, 023901 (2019).

[Crossref]

M. Hermans, M. Burm, T. Van Vaerenbergh, J. Dambre, and P. Bienstman, “Trainable hardware for dynamical computing using error backpropagation through physical media,” Nat. Commun. 6, 6729 (2015).

[Crossref]

M. Hermans, J. Dambre, and P. Bienstman, “Optoelectronic systems trained with backpropagation through time,” IEEE Trans. Neural Netw. Learn. Syst. 26, 1545–1550 (2014).

[Crossref]

A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med. 25, 24–29 (2019).

[Crossref]

B. Marr, B. Degnan, P. Hasler, and D. Anderson, “Scaling energy per operation via an asynchronous pipeline,” IEEE Trans. Very Large Scale Integr. Syst. 21, 147–151 (2012).

[Crossref]

J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, and W. He, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature 572, 106–111 (2019).

[Crossref]

G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag. 29, 82–97 (2012).

[Crossref]

T. Deng, J. Robertson, Z.-M. Wu, G.-Q. Xia, X.-D. Lin, X. Tang, Z.-J. Wang, and A. Huartado, “Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks,” IEEE Access 6, 67951–67958 (2018).

[Crossref]

J. Robertson, T. Deng, J. Javaloyes, and A. Hurtado, “Controlled inhibition of spiking dynamics in VCSELs for neuromorphic photonics: theory and experiments,” Opt. Lett. 42, 1560–1563 (2017).

[Crossref]

A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med. 25, 24–29 (2019).

[Crossref]

P. Minzioni, C. Lacava, T. Tanabe, J. Dong, X. Hu, G. Csaba, W. Porod, G. Singh, A. E. Willner, and A. Almaiman, “Roadmap on all-optical processing,” J. Opt. 21, 063001 (2019).

[Crossref]

Y. Zuo, B. Li, Y. Zhao, Y. Jiang, Y.-C. Chen, P. Chen, G.-B. Jo, J. Liu, and S. Du, “All optical neural network with nonlinear activation functions,” Optica 6, 1132–1137 (2019).

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

[Crossref]

T. W. Hughes, R. J. England, and S. Fan, “Reconfigurable photonic circuit for controlled power delivery to laser-driven accelerators on a chip,” Phys. Rev. Appl. 11, 064014 (2019).

[Crossref]

R. Hamerly, L. Bernstein, A. Sludds, M. Soljačić, and D. Englund, “Large-scale optical neural networks based on photoelectric multiplication,” Phys. Rev. X 9, 021032 (2019).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, and D. Englund, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med. 25, 24–29 (2019).

[Crossref]

T. Yan, J. Wu, T. Zhou, H. Xie, F. Xu, J. Fan, L. Fang, X. Lin, and Q. Dai, “Fourier-space diffractive deep neural network,” Phys. Rev. Lett. 123, 023901 (2019).

[Crossref]

T. W. Hughes, R. J. England, and S. Fan, “Reconfigurable photonic circuit for controlled power delivery to laser-driven accelerators on a chip,” Phys. Rev. Appl. 11, 064014 (2019).

[Crossref]

T. W. Hughes, M. Minkov, Y. Shi, and S. Fan, “Training of photonic neural networks through in situ backpropagation and gradient measurement,” Optica 5, 864–871 (2018).

[Crossref]

T. W. Hughes, I. A. Williamson, M. Minkov, and S. Fan, “Wave physics as an analog recurrent neural network,” arXiv:1904.12831 (2019).

T. Yan, J. Wu, T. Zhou, H. Xie, F. Xu, J. Fan, L. Fang, X. Lin, and Q. Dai, “Fourier-space diffractive deep neural network,” Phys. Rev. Lett. 123, 023901 (2019).

[Crossref]

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

[Crossref]

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283–292 (2012).

[Crossref]

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26, 7600108 (2019).

[Crossref]

J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner, “Reinforcement learning in a large-scale photonic recurrent neural network,” Optica 5, 756–760 (2018).

[Crossref]

Q. Zhang, H. Yu, M. Barbiero, B. Wang, and M. Gu, “Artificial neural networks enabled by nanophotonics,” Light Sci. Appl. 8, 1 (2019).

[Crossref]

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot, “Mastering the game of go with deep neural networks and tree search,” Nature 529, 484–489 (2016).

[Crossref]

P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science 345, 668–673 (2014).

[Crossref]

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).

[Crossref]

R. Hamerly, L. Bernstein, A. Sludds, M. Soljačić, and D. Englund, “Large-scale optical neural networks based on photoelectric multiplication,” Phys. Rev. X 9, 021032 (2019).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, and D. Englund, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

B. Marr, B. Degnan, P. Hasler, and D. Anderson, “Scaling energy per operation via an asynchronous pipeline,” IEEE Trans. Very Large Scale Integr. Syst. 21, 147–151 (2012).

[Crossref]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778.

J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, and W. He, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature 572, 106–111 (2019).

[Crossref]

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

[Crossref]

M. Hermans, M. Burm, T. Van Vaerenbergh, J. Dambre, and P. Bienstman, “Trainable hardware for dynamical computing using error backpropagation through physical media,” Nat. Commun. 6, 6729 (2015).

[Crossref]

M. Hermans, J. Dambre, and P. Bienstman, “Optoelectronic systems trained with backpropagation through time,” IEEE Trans. Neural Netw. Learn. Syst. 26, 1545–1550 (2014).

[Crossref]

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

[Crossref]

G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag. 29, 82–97 (2012).

[Crossref]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), pp. 1097–1105.

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, and D. Englund, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

P. Minzioni, C. Lacava, T. Tanabe, J. Dong, X. Hu, G. Csaba, W. Porod, G. Singh, A. E. Willner, and A. Almaiman, “Roadmap on all-optical processing,” J. Opt. 21, 063001 (2019).

[Crossref]

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot, “Mastering the game of go with deep neural networks and tree search,” Nature 529, 484–489 (2016).

[Crossref]

T. Deng, J. Robertson, Z.-M. Wu, G.-Q. Xia, X.-D. Lin, X. Tang, Z.-J. Wang, and A. Huartado, “Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks,” IEEE Access 6, 67951–67958 (2018).

[Crossref]

T. W. Hughes, R. J. England, and S. Fan, “Reconfigurable photonic circuit for controlled power delivery to laser-driven accelerators on a chip,” Phys. Rev. Appl. 11, 064014 (2019).

[Crossref]

T. W. Hughes, M. Minkov, Y. Shi, and S. Fan, “Training of photonic neural networks through in situ backpropagation and gradient measurement,” Optica 5, 864–871 (2018).

[Crossref]

T. W. Hughes, I. A. Williamson, M. Minkov, and S. Fan, “Wave physics as an analog recurrent neural network,” arXiv:1904.12831 (2019).

P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science 345, 668–673 (2014).

[Crossref]

P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science 345, 668–673 (2014).

[Crossref]

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26, 7600108 (2019).

[Crossref]

J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner, “Reinforcement learning in a large-scale photonic recurrent neural network,” Optica 5, 756–760 (2018).

[Crossref]

L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification,” Phys. Rev. X 7, 011015 (2017).

[Crossref]

G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag. 29, 82–97 (2012).

[Crossref]

D. R. Solli and B. Jalali, “Analog optical computing,” Nat. Photonics 9, 704–706 (2015).

[Crossref]

X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).

[Crossref]

Y. Luo, D. Mengu, N. T. Yardimci, Y. Rivenson, M. Veli, M. Jarrahi, and A. Ozcan, “Design of task-specific optical systems using broadband diffractive neural networks,” arXiv:1909.06553 (2019).

H. Chen, S. Jayasuriya, J. Yang, J. Stephen, S. Sivaramakrishnan, A. Veeraraghavan, and A. Molnar, “ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 903–912.

Y. Zuo, B. Li, Y. Zhao, Y. Jiang, Y.-C. Chen, P. Chen, G.-B. Jo, J. Liu, and S. Du, “All optical neural network with nonlinear activation functions,” Optica 6, 1132–1137 (2019).

Y. Zuo, B. Li, Y. Zhao, Y. Jiang, Y.-C. Chen, P. Chen, G.-B. Jo, J. Liu, and S. Du, “All optical neural network with nonlinear activation functions,” Optica 6, 1132–1137 (2019).

G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag. 29, 82–97 (2012).

[Crossref]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), pp. 1097–1105.

A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med. 25, 24–29 (2019).

[Crossref]

P. Minzioni, C. Lacava, T. Tanabe, J. Dong, X. Hu, G. Csaba, W. Porod, G. Singh, A. E. Willner, and A. Almaiman, “Roadmap on all-optical processing,” J. Opt. 21, 063001 (2019).

[Crossref]

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283–292 (2012).

[Crossref]

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot, “Mastering the game of go with deep neural networks and tree search,” Nature 529, 484–489 (2016).

[Crossref]

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26, 7600108 (2019).

[Crossref]

J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner, “Reinforcement learning in a large-scale photonic recurrent neural network,” Optica 5, 756–760 (2018).

[Crossref]

L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification,” Phys. Rev. X 7, 011015 (2017).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, and D. Englund, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

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

[Crossref]

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).

[Crossref]

M. W. Matthès, P. del Hougne, J. de Rosny, G. Lerosey, and S. M. Popoff, “Optical complex media as universal reconfigurable linear operators,” Optica 6, 465–472 (2019).

[Crossref]

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283–292 (2012).

[Crossref]

Y. Zuo, B. Li, Y. Zhao, Y. Jiang, Y.-C. Chen, P. Chen, G.-B. Jo, J. Liu, and S. Du, “All optical neural network with nonlinear activation functions,” Optica 6, 1132–1137 (2019).

Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, and J. P. Strachan, “In situ training of feed-forward and recurrent convolutional memristor networks,” Nat. Mach. Intell. 1, 434–442 (2019).

[Crossref]

Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, and J. P. Strachan, “In situ training of feed-forward and recurrent convolutional memristor networks,” Nat. Mach. Intell. 1, 434–442 (2019).

[Crossref]

Y. Li, Y. Xue, and L. Tian, “Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media,” Optica 5, 1181–1190 (2018).

[Crossref]

Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, and J. P. Strachan, “In situ training of feed-forward and recurrent convolutional memristor networks,” Nat. Mach. Intell. 1, 434–442 (2019).

[Crossref]

T. Yan, J. Wu, T. Zhou, H. Xie, F. Xu, J. Fan, L. Fang, X. Lin, and Q. Dai, “Fourier-space diffractive deep neural network,” Phys. Rev. Lett. 123, 023901 (2019).

[Crossref]

X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).

[Crossref]

T. Deng, J. Robertson, Z.-M. Wu, G.-Q. Xia, X.-D. Lin, X. Tang, Z.-J. Wang, and A. Huartado, “Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks,” IEEE Access 6, 67951–67958 (2018).

[Crossref]

Y. Zuo, B. Li, Y. Zhao, Y. Jiang, Y.-C. Chen, P. Chen, G.-B. Jo, J. Liu, and S. Du, “All optical neural network with nonlinear activation functions,” Optica 6, 1132–1137 (2019).

X. Luo, “Engineering optics 2.0: a revolution in optical materials, devices, and systems,” ACS Photon. 5, 4724–4738 (2018).

[Crossref]

X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).

[Crossref]

Y. Luo, D. Mengu, N. T. Yardimci, Y. Rivenson, M. Veli, M. Jarrahi, and A. Ozcan, “Design of task-specific optical systems using broadband diffractive neural networks,” arXiv:1909.06553 (2019).

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot, “Mastering the game of go with deep neural networks and tree search,” Nature 529, 484–489 (2016).

[Crossref]

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26, 7600108 (2019).

[Crossref]

J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner, “Reinforcement learning in a large-scale photonic recurrent neural network,” Optica 5, 756–760 (2018).

[Crossref]

A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M. Bethge, DeepLabCut: Markerless Pose Estimation of User-Defined Body Parts with Deep Learning (Nature, 2018).

B. Marr, B. Degnan, P. Hasler, and D. Anderson, “Scaling energy per operation via an asynchronous pipeline,” IEEE Trans. Very Large Scale Integr. Syst. 21, 147–151 (2012).

[Crossref]

L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification,” Phys. Rev. X 7, 011015 (2017).

[Crossref]

A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M. Bethge, DeepLabCut: Markerless Pose Estimation of User-Defined Body Parts with Deep Learning (Nature, 2018).

A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M. Bethge, DeepLabCut: Markerless Pose Estimation of User-Defined Body Parts with Deep Learning (Nature, 2018).

C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” arXiv:1705.09792 (2017).

Y. Luo, D. Mengu, N. T. Yardimci, Y. Rivenson, M. Veli, M. Jarrahi, and A. Ozcan, “Design of task-specific optical systems using broadband diffractive neural networks,” arXiv:1909.06553 (2019).

P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science 345, 668–673 (2014).

[Crossref]

T. W. Hughes, M. Minkov, Y. Shi, and S. Fan, “Training of photonic neural networks through in situ backpropagation and gradient measurement,” Optica 5, 864–871 (2018).

[Crossref]

T. W. Hughes, I. A. Williamson, M. Minkov, and S. Fan, “Wave physics as an analog recurrent neural network,” arXiv:1904.12831 (2019).

P. Minzioni, C. Lacava, T. Tanabe, J. Dong, X. Hu, G. Csaba, W. Porod, G. Singh, A. E. Willner, and A. Almaiman, “Roadmap on all-optical processing,” J. Opt. 21, 063001 (2019).

[Crossref]

J. M. Shainline, S. M. Buckley, R. P. Mirin, and S. W. Nam, “Superconducting optoelectronic circuits for neuromorphic computing,” Phys. Rev. Appl. 7, 034013 (2017).

[Crossref]

G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag. 29, 82–97 (2012).

[Crossref]

H. Chen, S. Jayasuriya, J. Yang, J. Stephen, S. Sivaramakrishnan, A. Veeraraghavan, and A. Molnar, “ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 903–912.

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283–292 (2012).

[Crossref]

A. Mathis, P. Mamidanna, K. M. Cury, T. Abe, V. N. Murthy, M. W. Mathis, and M. Bethge, DeepLabCut: Markerless Pose Estimation of User-Defined Body Parts with Deep Learning (Nature, 2018).

P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science 345, 668–673 (2014).

[Crossref]

J. M. Shainline, S. M. Buckley, R. P. Mirin, and S. W. Nam, “Superconducting optoelectronic circuits for neuromorphic computing,” Phys. Rev. Appl. 7, 034013 (2017).

[Crossref]

D. Woods and T. J. Naughton, “Optical computing: photonic neural networks,” Nat. Phys. 8, 257–259 (2012).

[Crossref]

G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag. 29, 82–97 (2012).

[Crossref]

Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, and J. P. Strachan, “In situ training of feed-forward and recurrent convolutional memristor networks,” Nat. Mach. Intell. 1, 434–442 (2019).

[Crossref]

G. Barbastathis, A. Ozcan, and G. Situ, “On the use of deep learning for computational imaging,” Optica 6, 921–943 (2019).

[Crossref]

X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).

[Crossref]

Y. Luo, D. Mengu, N. T. Yardimci, Y. Rivenson, M. Veli, M. Jarrahi, and A. Ozcan, “Design of task-specific optical systems using broadband diffractive neural networks,” arXiv:1909.06553 (2019).

C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” arXiv:1705.09792 (2017).

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot, “Mastering the game of go with deep neural networks and tree search,” Nature 529, 484–489 (2016).

[Crossref]

J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, and W. He, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature 572, 106–111 (2019).

[Crossref]

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

[Crossref]

P. Minzioni, C. Lacava, T. Tanabe, J. Dong, X. Hu, G. Csaba, W. Porod, G. Singh, A. E. Willner, and A. Almaiman, “Roadmap on all-optical processing,” J. Opt. 21, 063001 (2019).

[Crossref]

S. Maktoobi, L. Froehly, L. Andreoli, X. Porte, M. Jacquot, L. Larger, and D. Brunner, “Diffractive coupling for photonic networks: how big can we go?” IEEE J. Sel. Top. Quantum Electron. 26, 7600108 (2019).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, and D. Englund, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

P. R. Prucnal and B. J. Shastri, Neuromorphic Photonics (CRC Press, 2017).

Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, and J. P. Strachan, “In situ training of feed-forward and recurrent convolutional memristor networks,” Nat. Mach. Intell. 1, 434–442 (2019).

[Crossref]

A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med. 25, 24–29 (2019).

[Crossref]

Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, and J. P. Strachan, “In situ training of feed-forward and recurrent convolutional memristor networks,” Nat. Mach. Intell. 1, 434–442 (2019).

[Crossref]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778.

X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).

[Crossref]

Y. Luo, D. Mengu, N. T. Yardimci, Y. Rivenson, M. Veli, M. Jarrahi, and A. Ozcan, “Design of task-specific optical systems using broadband diffractive neural networks,” arXiv:1909.06553 (2019).

T. Deng, J. Robertson, Z.-M. Wu, G.-Q. Xia, X.-D. Lin, X. Tang, Z.-J. Wang, and A. Huartado, “Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks,” IEEE Access 6, 67951–67958 (2018).

[Crossref]

J. Robertson, T. Deng, J. Javaloyes, and A. Hurtado, “Controlled inhibition of spiking dynamics in VCSELs for neuromorphic photonics: theory and experiments,” Opt. Lett. 42, 1560–1563 (2017).

[Crossref]

A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med. 25, 24–29 (2019).

[Crossref]

C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” arXiv:1705.09792 (2017).

I. Chakraborty, G. Saha, and K. Roy, “Photonic in-memory computing primitive for spiking neural networks using phase-change materials,” Phys. Rev. Appl. 11, 014063 (2019).

[Crossref]

I. Chakraborty, G. Saha, and K. Roy, “Photonic in-memory computing primitive for spiking neural networks using phase-change materials,” Phys. Rev. Appl. 11, 014063 (2019).

[Crossref]

C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” arXiv:1705.09792 (2017).

P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “A million spiking-neuron integrated circuit with a scalable communication network and interface,” Science 345, 668–673 (2014).

[Crossref]

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot, “Mastering the game of go with deep neural networks and tree search,” Nature 529, 484–489 (2016).

[Crossref]

G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag. 29, 82–97 (2012).

[Crossref]

C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” arXiv:1705.09792 (2017).

J. M. Shainline, S. M. Buckley, R. P. Mirin, and S. W. Nam, “Superconducting optoelectronic circuits for neuromorphic computing,” Phys. Rev. Appl. 7, 034013 (2017).

[Crossref]

P. R. Prucnal and B. J. Shastri, Neuromorphic Photonics (CRC Press, 2017).

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, and D. Englund, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

G. K. Shirmanesh, R. Sokhoyan, P. C. Wu, and H. A. Atwater, “Electro-optically tunable universal metasurfaces,” arXiv:1910.02069 (2019).

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot, “Mastering the game of go with deep neural networks and tree search,” Nature 529, 484–489 (2016).

[Crossref]

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot, “Mastering the game of go with deep neural networks and tree search,” Nature 529, 484–489 (2016).

[Crossref]

P. Minzioni, C. Lacava, T. Tanabe, J. Dong, X. Hu, G. Csaba, W. Porod, G. Singh, A. E. Willner, and A. Almaiman, “Roadmap on all-optical processing,” J. Opt. 21, 063001 (2019).

[Crossref]

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

[Crossref]

H. Chen, S. Jayasuriya, J. Yang, J. Stephen, S. Sivaramakrishnan, A. Veeraraghavan, and A. Molnar, “ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 903–912.

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, and D. Englund, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

R. Hamerly, L. Bernstein, A. Sludds, M. Soljačić, and D. Englund, “Large-scale optical neural networks based on photoelectric multiplication,” Phys. Rev. X 9, 021032 (2019).

[Crossref]

G. K. Shirmanesh, R. Sokhoyan, P. C. Wu, and H. A. Atwater, “Electro-optically tunable universal metasurfaces,” arXiv:1910.02069 (2019).

R. Hamerly, L. Bernstein, A. Sludds, M. Soljačić, and D. Englund, “Large-scale optical neural networks based on photoelectric multiplication,” Phys. Rev. X 9, 021032 (2019).

[Crossref]

D. R. Solli and B. Jalali, “Analog optical computing,” Nat. Photonics 9, 704–706 (2015).

[Crossref]

J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, and W. He, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature 572, 106–111 (2019).

[Crossref]

Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, and J. P. Strachan, “In situ training of feed-forward and recurrent convolutional memristor networks,” Nat. Mach. Intell. 1, 434–442 (2019).

[Crossref]

G. Van der Sande, D. Brunner, and M. C. Soriano, “Advances in photonic reservoir computing,” Nanophotonics 6, 561–576 (2017).

[Crossref]

H. Chen, S. Jayasuriya, J. Yang, J. Stephen, S. Sivaramakrishnan, A. Veeraraghavan, and A. Molnar, “ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 903–912.

Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, and J. P. Strachan, “In situ training of feed-forward and recurrent convolutional memristor networks,” Nat. Mach. Intell. 1, 434–442 (2019).

[Crossref]

C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” arXiv:1705.09792 (2017).

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778.

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, and D. Englund, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (2012), pp. 1097–1105.

P. Minzioni, C. Lacava, T. Tanabe, J. Dong, X. Hu, G. Csaba, W. Porod, G. Singh, A. E. Willner, and A. Almaiman, “Roadmap on all-optical processing,” J. Opt. 21, 063001 (2019).

[Crossref]

T. Deng, J. Robertson, Z.-M. Wu, G.-Q. Xia, X.-D. Lin, X. Tang, Z.-J. Wang, and A. Huartado, “Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks,” IEEE Access 6, 67951–67958 (2018).

[Crossref]

A. Esteva, A. Robicquet, B. Ramsundar, V. Kuleshov, M. DePristo, K. Chou, C. Cui, G. Corrado, S. Thrun, and J. Dean, “A guide to deep learning in healthcare,” Nat. Med. 25, 24–29 (2019).

[Crossref]

C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” arXiv:1705.09792 (2017).

L. Larger, A. Baylón-Fuentes, R. Martinenghi, V. S. Udaltsov, Y. K. Chembo, and M. Jacquot, “High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification,” Phys. Rev. X 7, 011015 (2017).

[Crossref]

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, and M. Lanctot, “Mastering the game of go with deep neural networks and tree search,” Nature 529, 484–489 (2016).

[Crossref]

G. Van der Sande, D. Brunner, and M. C. Soriano, “Advances in photonic reservoir computing,” Nanophotonics 6, 561–576 (2017).

[Crossref]

M. Hermans, M. Burm, T. Van Vaerenbergh, J. Dambre, and P. Bienstman, “Trainable hardware for dynamical computing using error backpropagation through physical media,” Nat. Commun. 6, 6729 (2015).

[Crossref]

G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag. 29, 82–97 (2012).

[Crossref]

H. Chen, S. Jayasuriya, J. Yang, J. Stephen, S. Sivaramakrishnan, A. Veeraraghavan, and A. Molnar, “ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 903–912.

X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).

[Crossref]

Y. Luo, D. Mengu, N. T. Yardimci, Y. Rivenson, M. Veli, M. Jarrahi, and A. Ozcan, “Design of task-specific optical systems using broadband diffractive neural networks,” arXiv:1909.06553 (2019).

Q. Zhang, H. Yu, M. Barbiero, B. Wang, and M. Gu, “Artificial neural networks enabled by nanophotonics,” Light Sci. Appl. 8, 1 (2019).

[Crossref]

J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, and W. He, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature 572, 106–111 (2019).

[Crossref]

Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, and J. P. Strachan, “In situ training of feed-forward and recurrent convolutional memristor networks,” Nat. Mach. Intell. 1, 434–442 (2019).

[Crossref]

T. Deng, J. Robertson, Z.-M. Wu, G.-Q. Xia, X.-D. Lin, X. Tang, Z.-J. Wang, and A. Huartado, “Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks,” IEEE Access 6, 67951–67958 (2018).

[Crossref]

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

[Crossref]

J. Chang and G. Wetzstein, “Deep optics for monocular depth estimation and 3D object detection,” arXiv:1904.08601 (2019).

T. W. Hughes, I. A. Williamson, M. Minkov, and S. Fan, “Wave physics as an analog recurrent neural network,” arXiv:1904.12831 (2019).

P. Minzioni, C. Lacava, T. Tanabe, J. Dong, X. Hu, G. Csaba, W. Porod, G. Singh, A. E. Willner, and A. Almaiman, “Roadmap on all-optical processing,” J. Opt. 21, 063001 (2019).

[Crossref]

D. Woods and T. J. Naughton, “Optical computing: photonic neural networks,” Nat. Phys. 8, 257–259 (2012).

[Crossref]

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

[Crossref]

T. Yan, J. Wu, T. Zhou, H. Xie, F. Xu, J. Fan, L. Fang, X. Lin, and Q. Dai, “Fourier-space diffractive deep neural network,” Phys. Rev. Lett. 123, 023901 (2019).

[Crossref]

G. K. Shirmanesh, R. Sokhoyan, P. C. Wu, and H. A. Atwater, “Electro-optically tunable universal metasurfaces,” arXiv:1910.02069 (2019).

J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, and W. He, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature 572, 106–111 (2019).

[Crossref]

J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, and W. He, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature 572, 106–111 (2019).

[Crossref]

T. Deng, J. Robertson, Z.-M. Wu, G.-Q. Xia, X.-D. Lin, X. Tang, Z.-J. Wang, and A. Huartado, “Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks,” IEEE Access 6, 67951–67958 (2018).

[Crossref]

T. Deng, J. Robertson, Z.-M. Wu, G.-Q. Xia, X.-D. Lin, X. Tang, Z.-J. Wang, and A. Huartado, “Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks,” IEEE Access 6, 67951–67958 (2018).

[Crossref]

T. Yan, J. Wu, T. Zhou, H. Xie, F. Xu, J. Fan, L. Fang, X. Lin, and Q. Dai, “Fourier-space diffractive deep neural network,” Phys. Rev. Lett. 123, 023901 (2019).

[Crossref]

T. Yan, J. Wu, T. Zhou, H. Xie, F. Xu, J. Fan, L. Fang, X. Lin, and Q. Dai, “Fourier-space diffractive deep neural network,” Phys. Rev. Lett. 123, 023901 (2019).

[Crossref]

Z. Wang, C. Li, P. Lin, M. Rao, Y. Nie, W. Song, Q. Qiu, Y. Li, P. Yan, and J. P. Strachan, “In situ training of feed-forward and recurrent convolutional memristor networks,” Nat. Mach. Intell. 1, 434–442 (2019).

[Crossref]

T. Yan, J. Wu, T. Zhou, H. Xie, F. Xu, J. Fan, L. Fang, X. Lin, and Q. Dai, “Fourier-space diffractive deep neural network,” Phys. Rev. Lett. 123, 023901 (2019).

[Crossref]

H. Chen, S. Jayasuriya, J. Yang, J. Stephen, S. Sivaramakrishnan, A. Veeraraghavan, and A. Molnar, “ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 903–912.

X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, and A. Ozcan, “All-optical machine learning using diffractive deep neural networks,” Science 361, 1004–1008 (2018).

[Crossref]

Y. Luo, D. Mengu, N. T. Yardimci, Y. Rivenson, M. Veli, M. Jarrahi, and A. Ozcan, “Design of task-specific optical systems using broadband diffractive neural networks,” arXiv:1909.06553 (2019).

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

[Crossref]

G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition,” IEEE Signal Process. Mag. 29, 82–97 (2012).

[Crossref]

Q. Zhang, H. Yu, M. Barbiero, B. Wang, and M. Gu, “Artificial neural networks enabled by nanophotonics,” Light Sci. Appl. 8, 1 (2019).

[Crossref]

Q. Zhang, H. Yu, M. Barbiero, B. Wang, and M. Gu, “Artificial neural networks enabled by nanophotonics,” Light Sci. Appl. 8, 1 (2019).

[Crossref]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778.

J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, and W. He, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature 572, 106–111 (2019).

[Crossref]

C. Trabelsi, O. Bilaniuk, Y. Zhang, D. Serdyuk, S. Subramanian, J. F. Santos, S. Mehri, N. Rostamzadeh, Y. Bengio, and C. J. Pal, “Deep complex networks,” arXiv:1705.09792 (2017).

J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, and W. He, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature 572, 106–111 (2019).

[Crossref]

Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, and D. Englund, “Deep learning with coherent nanophotonic circuits,” Nat. Photonics 11, 441–446 (2017).

[Crossref]

Y. Zuo, B. Li, Y. Zhao, Y. Jiang, Y.-C. Chen, P. Chen, G.-B. Jo, J. Liu, and S. Du, “All optical neural network with nonlinear activation functions,” Optica 6, 1132–1137 (2019).

T. Yan, J. Wu, T. Zhou, H. Xie, F. Xu, J. Fan, L. Fang, X. Lin, and Q. Dai, “Fourier-space diffractive deep neural network,” Phys. Rev. Lett. 123, 023901 (2019).

[Crossref]

J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, and W. He, “Towards artificial general intelligence with hybrid Tianjic chip architecture,” Nature 572, 106–111 (2019).

[Crossref]

Y. Zuo, B. Li, Y. Zhao, Y. Jiang, Y.-C. Chen, P. Chen, G.-B. Jo, J. Liu, and S. Du, “All optical neural network with nonlinear activation functions,” Optica 6, 1132–1137 (2019).