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Deep learning has rapidly become a widespread tool in both scientific and commercial endeavors.
Optics Letters 2
J. W. Goodman, A. Dias, and L. Woody, Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms · 1978
Earlier work this paper cites.
Optics Letters 2
J. W. Goodman, A. Dias, and L. Woody, Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms · 1978
Earlier work this paper cites.
Applied Optics 27
D. Psaltis, D. Brady, and K. Wagner, Adaptive optical networks using photorefractive crystals · 1988
Earlier work this paper cites.
Japanese Journal of Applied Physics 31
Y. Hayasaki, I. Tohyama, T. Yatagai, M. Mori, and S. Ishihara, Optical learning neural network using Selfoc microlens array · 1992
Earlier work this paper cites.
Japanese Journal of Applied Physics 31
Y. Hayasaki, I. Tohyama, T. Yatagai, M. Mori, and S. Ishihara, Optical learning neural network using Selfoc microlens array · 1992
Earlier work this paper cites.
Nature Photonics 2
L. Tang, S. E. Kocabas, S. Latif, A. K. Okyay, D.-S. Ly-Gagnon, K. C. Saraswat, and D. A. Miller, Nanometre-scale germanium photodetector enhanced by a near-infrared dipole antenna · 2008
Earlier work this paper cites.
Nature Photonics 4
H. J. Caulfield and S. Dolev, Why future supercomputing requires optics · 2010
Earlier work this paper cites.
A. Coates, A. Ng, and H. Lee, An analysis of single-layer networks in unsupervised feature learning . In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS)
2011
Earlier work this paper cites.
X. Glorot, A. Bordes, and Y. Bengio, Deep sparse rectifier neural networks . In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS)
2011
Earlier work this paper cites.
Sensors 13
A. Hemmi, R. Mizumura, R. Kawanishi, H. Nakajima, H. Zeng, K. Uchiyama, N. Kaneki, and T. Imato, Development of a novel two dimensional surface plasmon resonance sensor using multiplied beam splitting optics · 2013
Earlier work this paper cites.
M. Horowitz, Computing’s energy problem (and what we can do about it) . In 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC)
2014
Earlier work this paper cites.
Nature 521
Y. LeCun, Y. Bengio, and G. Hinton, Deep learning · 2015
Earlier work this paper cites.
IEEE Transactions on Electron Devices 62
G. W. Burr, R. M. Shelby, S. Sidler, C. Di Nolfo, J. Jang, I. Boybat, R. S. Shenoy, P. Narayanan, K. Virwani, E. U. Giacometti, B. Kurdi, and H. Hwang, Experimental demonstration and tolerancing of a large-scale neural network (165 000 synapses) using phase-change memory as the synaptic weight element · 2015
Earlier work this paper cites.
Nature Photonics 9
N. Youngblood, C. Chen, S. J. Koester, and M. Li, Waveguide-integrated black phosphorus photodetector with high responsivity and low dark current · 2015
Earlier work this paper cites.
In Advances in Neural Information Processing Systems 28 (NeurIPS 2015), 28 (2015)
C. De Sa, C. Zhang, K. Olukotun, and C. Ré, Taming the wild: A unified analysis of hogwild!-style algorithms · 2015
Earlier work this paper cites.
Science 354
P. L. McMahon, A. Marandi, Y. Haribara, R. Hamerly, C. Langrock, S. Tamate, T. Inagaki, H. Takesue, S. Utsunomiya, K. Aihara, R. L. Byer, M. M. Fejer, H. Mabuchi, and Y. Yamamoto, A fully programmable 100-spin coherent Ising machine with all-to-all connections · 2016
Earlier work this paper cites.
Science 354
T. Inagaki, Y. Haribara, K. Igarashi, T. Sonobe, S. Tamate, T. Honjo, A. Marandi, P. L. McMahon, T. Umeki, K. Enbutsu, O. Tadanaga, H. Takenouchi, K. Aihara, K.-i. Kawarabayashi, K. Inoue, S. Utsunomiya, and H. Takesue, A coherent Ising machine for 2000-node optimization problems · 2016
Earlier work this paper cites.
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, Binarized neural networks . In Advances in Neural Information Processing Systems 29 (NeurIPS 2016)
2016
Earlier work this paper cites.
Journal of Big Data 6
C. Shorten and T. M. Khoshgoftaar, A survey on image data augmentation for deep learning · 2016
Earlier work this paper cites.
Optica 3
K. Nozaki, S. Matsuo, T. Fujii, K. Takeda, M. Ono, A. Shakoor, E. Kuramochi, and M. Notomi, Photonic-crystal nano-photodetector with ultrasmall capacitance for on-chip light-to-voltage conversion without an amplifier · 2016
Earlier work this paper cites.
B. Xu, Y. Zhou, and Y. Chiu, A 23mW 24GS/s 6b time-interleaved hybrid two-step ADC in 28nm CMOS . In Proceedings of the IEEE Symposium on VLSI Circuits (VLSI-Circuits)
2016
Earlier work this paper cites.
Proceedings of the IEEE 105
V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, Efficient processing of deep neural networks: A tutorial and survey · 2017
Earlier work this paper cites.
Nature Photonics 11
Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund, and M. Soljačić, Deep learning with coherent nanophotonic circuits · 2017
Earlier work this paper cites.
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, R. Boyle, P.-l. Cantin, C. Chao, C. Clark, C. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmagham et al. In-datacenter performance analysis of a tensor processing unit . In Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA)
2017
Cited alongside, same era.
Journal of Lightwave Technology 35
D. A. B. Miller, Attojoule optoelectronics for low-energy information processing and communications · 2017
Cited alongside, same era.
Proceedings of the IEEE 105
V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, Efficient processing of deep neural networks: A tutorial and survey · 2017
Cited alongside, same era.
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, R. Boyle, P.-l. Cantin, C. Chao, C. Clark, C. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmagham et al. In-datacenter performance analysis of a tensor processing unit . In Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA)
N. C. Thompson, K. Greenewald, K. Lee, and G. F. Manso, The computational limits of deep learning · 2020
Later among the works it cites.
Nature 588
G. Wetzstein, A. Ozcan, S. Gigan, S. Fan, D. Englund, M. Soljačić, C. Denz, D. A. B. Miller, and D. Psaltis, Inference in artificial intelligence with deep optics and photonics · 2020
Later among the works it cites.
IEEE Journal of Selected Topics in Quantum Electronics 26
M. A. Nahmias, T. F. De Lima, A. N. Tait, H.-T. Peng, B. J. Shastri, and P. R. Prucnal, Photonic multiply-accumulate operations for neural networks · 2020
Later among the works it cites.
A. Reuther, P. Michaleas, M. Jones, V. Gadepally, S. Samsi, and J. Kepner, Survey of machine learning accelerators · 2020
Later among the works it cites.
Nanophotonics 9
P. Stark, F. Horst, R. Dangel, J. Weiss, and B. J. Offrein, Opportunities for integrated photonic neural networks · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
Science 361
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 · 2018
Cited alongside, same era.
Scientific Reports 8
J. Chang, V. Sitzmann, X. Dun, W. Heidrich, and G. Wetzstein, Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification · 2018
Cited alongside, same era.
Optica 5
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 · 2018
Cited alongside, same era.
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko, Quantization and training of neural networks for efficient integer-arithmetic-only inference . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2018
Cited alongside, same era.
Nature 562
C. Wang, M. Zhang, X. Chen, M. Bertrand, A. Shams-Ansari, S. Chandrasekhar, P. Winzer, and M. Lončar, Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages · 2018
Cited alongside, same era.
Physical Review X 9
R. Hamerly, L. Bernstein, A. Sludds, M. Soljačić, and D. Englund, Large-scale optical neural networks based on photoelectric multiplication · 2019
Cited alongside, same era.
URL: www.youtube.com/watch?v=7-31KgImGgU (2019)
A. Jassy, Keynote address at AWS re:Invent · 2019
Cited alongside, same era.
Physical Review Applied 11
A. N. Tait, T. F. De Lima, M. A. Nahmias, H. B. Miller, H.-T. Peng, B. J. Shastri, and P. R. Prucnal, Silicon photonic modulator neuron · 2019
Cited alongside, same era.
Nature 586
W. Bogaerts, D. Pérez, J. Capmany, D. A. B. Miller, J. Poon, D. Englund, F. Morichetti, and A. Melloni, Programmable photonic circuits · 2020
Later among the works it cites.
Optica 7
M. Miscuglio, Z. Hu, S. Li, J. K. George, R. Capanna, H. Dalir, P. M. Bardet, P. Gupta, and V. J. Sorger, Massively parallel amplitude-only Fourier neural network · 2020
Later among the works it cites.
Optics Letters 45
J. Spall, X. Guo, T. D. Barrett, and A. Lvovsky, Fully reconfigurable coherent optical vector–matrix multiplication · 2020
Later among the works it cites.
C. Ramey, Silicon photonics for artificial intelligence acceleration . In Proceedings of the IEEE Hot Chips 32 Symposium (HCS)
2020
Later among the works it cites.
X. Xu, M. Tan, B. Corcoran, J. Wu, T. G. Nguyen, A. Boes, S. T. Chu, B. E. Little, R. Morandotti, A. Mitchell, D. G. Hicks, and D. J. Moss, Photonic perceptron based on a soliton crystal Kerr microcomb for high-speed, scalable, optical neural networks · 2020
Later among the works it cites.
Advanced Materials Technologies 5
Y. Su, Y. Zhang, C. Qiu, X. Guo, and L. Sun, Silicon photonic platform for passive waveguide devices: materials, fabrication, and applications · 2020
Later among the works it cites.
Optica 7
M. Prabhu, C. Roques-Carmes, Y. Shen, N. Harris, L. Jing, J. Carolan, R. Hamerly, T. Baehr-Jones, M. Hochberg, V. Čeperić, J. D. Joannopoulos, D. R. Englund, and M. Soljačić, Accelerating recurrent Ising machines in photonic integrated circuits · 2020
Later among the works it cites.
A. Reuther, P. Michaleas, M. Jones, V. Gadepally, S. Samsi, and J. Kepner, Survey of machine learning accelerators · 2020
Later among the works it cites.
Online Accessed: 2021-02-18 (2020)
B. Murmann, ADC Performance Survey 1997-2020 · 2020
Later among the works it cites.
C. Ramey, Silicon photonics for artificial intelligence acceleration . In Proceedings of the IEEE Hot Chips 32 Symposium (HCS)
2020
Later among the works it cites.
IEEE, International roadmap for devices and systems 2020 edition. IEEE IRDS™
2020
Later among the works it cites.
Nature Photonics 15
B. J. Shastri, A. N. Tait, T. F. de Lima, W. H. Pernice, H. Bhaskaran, C. D. Wright, and P. R. Prucnal, Photonics for artificial intelligence and neuromorphic computing · 2021
Closest in time.
Nature 589
J. Feldmann, N. Youngblood, M. Karpov, H. Gehring, X. Li, M. Stappers, M. Le Gallo, X. Fu, A. Lukashchuk, A. S. Raja, C. D. Wright, A. Sebastian, T. J. Kippenberg, W. H. P. Pernice, and H. Bhaskaran, Parallel convolutional processing using an integrated photonic tensor core · 2021
Closest in time.
Nature 589
X. Xu, M. Tan, B. Corcoran, J. Wu, A. Boes, T. G. Nguyen, S. T. Chu, B. E. Little, D. G. Hicks, R. Morandotti, A. Mitchell, and D. J. Moss, 11 TOPS photonic convolutional accelerator for optical neural networks · 2021
Closest in time.
Nature Communications 12
C. Wu, H. Yu, S. Lee, R. Peng, I. Takeuchi, and M. Li, Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network · 2021
Closest in time.
Scientific Reports 11
L. Bernstein, A. Sludds, R. Hamerly, V. Sze, J. Emer, and D. Englund, Freely scalable and reconfigurable optical hardware for deep learning · 2021
Closest in time.
Nature Photonics (2021)
T. Zhou, X. Lin, J. Wu, Y. Chen, H. Xie, Y. Li, J. Fan, H. Wu, L. Fang, and Q. Dai, Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit · 2021
Closest in time.
Japanese Journal of Applied Physics 60
D. Caimi, M. Sousa, S. Karg, and C. B. Zota, Scaled III–V-on-Si transistors for low-power logic and memory applications · 2021
Closest in time.