Deep learning with coherent nanophotonic circuits (original) (raw)
References
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature521, 436–444 (2015). ArticleADS Google Scholar
Silver, D. et al. Mastering the game of go with deep neural networks and tree search. Nature529, 484–489 (2016). ArticleADS Google Scholar
Mnih, V. et al. Human-level control through deep reinforcement learning. Nature518, 529–533 (2015). ArticleADS Google Scholar
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Proc.NIPS 1097–1105 (2012).
Esser, S. K. et al. Convolutional networks for fast, energy efficient neuromorphic computing. Proc. Natl Acad. Sci. USA113, 11441–11446 (2016). Article Google Scholar
Mead, C. Neuromorphic electronic systems. Proc. IEEE78, 1629–1636 (1990). Article Google Scholar
Poon, C.-S. & Zhou, K. Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities. Front. Neurosci. 5, 108 (2011). Article Google Scholar
Shafiee, A. et al. ISAAC: a convolutional neural network accelerator with in-situ analog arithmetic in crossbars. Proc. ISCA43, 14–26 (2016). Google Scholar
Misra, J. & Saha, I. Artificial neural networks in hardware: a survey of two decades of progress. Neurocomputing74, 239–255 (2010). Article Google Scholar
Chen, Y. H., Krishna, T., Emer, J. S. & Sze, V. Eyeriss: an energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE J. Solid-State Circuits52, 127–138 (2017). ArticleADS Google Scholar
Graves, A. et al. Hybrid computing using a neural network with dynamic external memory. Nature538, 471–476 (2016). ArticleADS Google Scholar
Tait, A. N., Nahmias, M. A., Tian, Y., Shastri, B. J. & Prucnal, P. R. in Nanophotonic Information Physics (ed. Naruse, M.) 183–222 (Springer, 2014).
Tait, A. N., Nahmias, M. A., Shastri, B. J. & Prucnal, P. R. Broadcast and weight: an integrated network for scalable photonic spike processing. J. Lightw. Technol. 32, 3427–3439 (2014). ArticleADS Google Scholar
Prucnal, P. R., Shastri, B. J., de Lima, T. F., Nahmias, M. A. & Tait, A. N. Recent progress in semiconductor excitable lasers for photonic spike processing. Adv. Opt. Phot. 8, 228–299 (2016). Article Google Scholar
Vandoorne, K. et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat. Commun. 5, 3541 (2014). ArticleADS Google Scholar
Appeltant, L. et al. Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011). ArticleADS Google Scholar
Larger, L. et al. Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. Opt. Express20, 3241–3249 (2012). ArticleADS Google Scholar
Paquot, Y. et al. Optoelectronic reservoir computing. Sci. Rep.2, 287 (2011). Article Google Scholar
Vivien, L. et al. Zero-bias 40gbit/s germanium waveguide photodetector on silicon. Opt. Express20, 1096–1101 (2012). ArticleADS Google Scholar
Cardenas, J. et al. Low loss etchless silicon photonic waveguides. Opt. Express17, 4752–4757 (2009). ArticleADS Google Scholar
Yang, L., Zhang, L. & Ji, R. On-chip optical matrix-vector multiplier. In SPIE Optical Engineering + Applications, 88550F (International Society for Optics and Photonics, 2013). Google Scholar
Farhat, N. H., Psaltis, D., Prata, A. & Paek, E. Optical implementation of the Hopfield model. Appl. Opt. 24, 1469–1475 (1985). ArticleADS Google Scholar
Harris, N. C. et al. Bosonic transport simulations in a large-scale programmable nanophotonic processor. Preprint at http://arXiv.org/abs/1507.03406 (2015).
Schmidhuber, J. Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015). Article Google Scholar
Lawson, C. L. & Hanson, R. J. Solving Least Squares Problems Vol. 15 (SIAM, 1995). Book Google Scholar
Miller, D. A. B. Perfect optics with imperfect components. Optica2, 747–750 (2015). ArticleADS Google Scholar
Reck, M., Zeilinger, A., Bernstein, H. J. & Bertani, P. Experimental realization of any discrete unitary operator. Phys. Rev. Lett. 73, 58–61 (1994). ArticleADS Google Scholar
Connelly, M. J. Semiconductor Optical Amplifiers (Springer Science & Business Media, 2007). Google Scholar
Bao, Q. et al. Monolayer graphene as a saturable absorber in a mode-locked laser. Nano Res. 4, 297–307 (2010). Article Google Scholar
Schirmer, R. W. & Gaeta, A. L. Nonlinear mirror based on two-photon absorption. J. Opt. Soc. Am. B14, 2865–2868 (1997). ArticleADS Google Scholar
Soljačić, M., Ibanescu, M., Johnson, S. G., Fink, Y. & Joannopoulos, J. Optimal bistable switching in nonlinear photonic crystals. Phys. Rev. E66, 055601 (2002). ArticleADS Google Scholar
Xu, B. & Ming, N.-B. Experimental observations of bistability and instability in a two-dimensional nonlinear optical superlattice. Phys. Rev. Lett. 71, 3959–3962 (1993). ArticleADS Google Scholar
Centeno, E. & Felbacq, D. Optical bistability infinite-size nonlinear bidimensional photonic crystals doped by a microcavity. Phys. Rev. B62, R7683–R7686 (2000). ArticleADS Google Scholar
Nozaki, K. et al. Sub-femtojoule all-optical switching using a photonic-crystal nanocavity. Nat. Photon. 4, 477–483 (2010). ArticleADS Google Scholar
Ríos, C. et al. Integrated all-photonic non-volatile multilevel memory. Nat. Photon. 9, 725–732 (2015). ArticleADS Google Scholar
Krizhevsky, A., Sutskever, I. & Hinton, G. E. in Imagenet Classification with Deep Convolutional Neural Networks (eds Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q.) 1097–1105 (Curran Associates, 2012).
Cheng, Z., Tsang, H. K., Wang, X., Xu, K. & Xu, J.-B. In-plane optical absorption and free carrier absorption in graphene-on-silicon waveguides. IEEE J. Sel. Top. Quantum Electron. 20, 43–48 (2014). ArticleADS Google Scholar
Chow, D. & Abdulla, W. H. in PRICAI 2004: Trends in Artificial Intelligence (eds Booth, R. & Zhang, M.-L.) 901–908 (Springer, 2004).
Deterding, D. H. Speaker Normalisation for Automatic Speech Recognition. PhD thesis, Univ. Cambridge (1990).
Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science313, 504–507 (2006). ArticleADSMathSciNet Google Scholar
Harris, N. C. et al. Efficient, compact and low loss thermo-optic phase shifter in silicon. Opt. Express22, 10487–10493 (2014). ArticleADS Google Scholar
Bertsimas, D. & Nohadani, O. Robust optimization with simulated annealing. J. Global Optim. 48, 323–334 (2010). ArticleMathSciNet Google Scholar
Wang, Q. et al. Optically reconfigurable metasurfaces and photonic devices based on phase change materials. Nat. Photon. 10, 60–65 (2016). ArticleADS Google Scholar
Tanabe, T., Notomi, M., Mitsugi, S., Shinya, A. & Kuramochi, E. Fast bistable all-optical switch and memory on a silicon photonic crystal on-chip. Opt. Lett. 30, 2575–2577 (2005). ArticleADS Google Scholar
Horowitz, M. Computing's energy problem. In 2014 IEEE Int. Solid-State Circuits Conf. Digest of Technical Papers (ISSCC) 10–14 (IEEE, 2014).
Arjovsky, M., Shah, A. & Bengio, Y. Unitary evolution recurrent neural networks. In Int. Conf. Machine Learning (2016).
Sun, J., Timurdogan, E., Yaacobi, A., Hosseini, E. S. & Watts, M. R. Large-scale nanophotonic phased array. Nature493, 195–199 (2013). ArticleADS Google Scholar
Rechtsman, M. C. et al. Photonic Floquet topological insulators. Nature496, 196–200 (2013). ArticleADS Google Scholar
Jia, Y. et al. Caffe: convolutional architecture for fast feature embedding. In Proc. 22nd ACM Int. Conf. Multimedia (MM ’14), 675–678 (ACM, 2014).
Sun, C. et al. Single-chip microprocessor that communicates directly using light. Nature528, 534–538 (2015). ArticleADS Google Scholar