Fetching the paper…
Reading the bibliography…
As deep neural networks (DNNs) grow to solve increasingly complex problems, they are becoming limited by the latency and power consumption of existing digital processors.
J. W. Goodman, A. R. Dias, and L. M. Woody, “Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms,” Opt. Lett. 2
1978
Earlier work this paper cites.
R. A. Athale and W. C. Collins, “Optical matrix–matrix multiplier based on outer product decomposition,” Applied Optics 21
1982
Earlier work this paper cites.
D. Psaltis and N. Farhat, “Optical information processing based on an associative-memory model of neural nets with thresholding and feedback,” Optics Letters 10
1985
Earlier work this paper cites.
K. Wagner and D. Psaltis, “Multilayer optical learning networks,” Applied Optics 26
1987
Earlier work this paper cites.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Computation 1
1989
Earlier work this paper cites.
J. W. Goodman, “4 decades of optical information processing,” Optics and Photonics News 2
1991
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE 86
1998
Earlier work this paper cites.
H. Zimmermann, A. Marchlewski, W. Gaberl, I. Jonak-Auer, G. Meinhardt, and E. Wachmann, “Blue-enhanced PIN finger photodiodes in a 0.35- μ \upmu m SiGe BiCMOS technology,” IEEE Photonics Technology Letters 21
2009
Earlier work this paper cites.
S. Latif, S. Kocabas, L. Tang, C. Debaes, and D. Miller, “Low capacitance CMOS silicon photodetectors for optical clock injection,” Applied Physics A 95
2009
Earlier work this paper cites.
B. E. Jonsson, “An empirical approach to finding energy efficient ADC architectures,” in Proc. of 2011 IMEKO IWADC & IEEE ADC Forum, (2011), pp. 1–6
2011
Earlier work this paper cites.
B. E. Jonsson, “An empirical approach to finding energy efficient ADC architectures,” in Proc. of 2011 IMEKO IWADC & IEEE ADC Forum, (2011), pp. 1–6
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25, (2012), pp. 1097–1105
2012
Earlier work this paper cites.
V. Tripathi and B. Murmann, “An 8-bit 450-MS/s single-bit/cycle SAR ADC in 65-nm CMOS,” in 2013 Proceedings of the ESSCIRC (ESSCIRC), (IEEE, 2013), pp. 117–120
2013
Earlier work this paper cites.
V. Tripathi and B. Murmann, “An 8-bit 450-MS/s single-bit/cycle SAR ADC in 65-nm CMOS,” in 2013 Proceedings of the ESSCIRC (ESSCIRC), (IEEE, 2013), pp. 117–120
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), (IEEE, 2014), pp. 10–14
2014
Earlier work this paper cites.
N. Mehta, C. Sun, M. Wade, S. Lin, M. Popovic, and V. Stojanović, “A 12Gb/s, 8.6 μ \upmu App input sensitivity, monolithic-integrated fully differential optical receiver in CMOS 45nm SOI process,” in ESSCIRC Conference 2016: 42nd European Solid-State Circuits Conference, (IEEE, 2016), pp. 491–494
2016
Earlier work this paper cites.
N. Mehta, C. Sun, M. Wade, S. Lin, M. Popovic, and V. Stojanović, “A 12Gb/s, 8.6 μ \upmu App input sensitivity, monolithic-integrated fully differential optical receiver in CMOS 45nm SOI process,” in ESSCIRC Conference 2016: 42nd European Solid-State Circuits Conference, (IEEE, 2016), pp. 491–494
2016
Earlier work this paper cites.
N. Vijaya Krishna Boppana and S. Ren, “A low-power and area-efficient 64-bit digital comparator,” Journal of Circuits, Systems and Computers 25
2016
Earlier work this paper cites.
E. Nurvitadhi, G. Venkatesh, J. Sim, D. Marr, R. Huang, J. Ong Gee Hock, Y. T. Liew, K. Srivatsan, D. Moss, S. Subhaschandra et al. , “Can FPGAs beat GPUs in accelerating next-generation deep neural networks?” in Proceedings of the 2017 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, (Association for Computing Machinery, New York, NY, USA, 2017), pp. 5–14
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 et al. , “In-datacenter performance analysis of a tensor processing unit,” in Proceedings of the 44th Annual International Symposium on Computer Architecture, (2017), pp. 1–12
2017
Earlier work this paper cites.
V. Sze, Y. Chen, T. Yang, and J. S. Emer, “Efficient processing of deep neural networks: A tutorial and survey,” Proc. IEEE 105
2017
Earlier work this paper cites.
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,” Nat. Photonics 11
2017
Earlier work this paper cites.
A. N. Tait, T. F. de Lima, E. Zhou, A. X. Wu, M. A. Nahmias, B. J. Shastri, and P. R. Prucnal, “Neuromorphic photonic networks using silicon photonic weight banks,” Sci. Rep. 7
2017
Earlier work this paper cites.
D. A. B. Miller, “Attojoule optoelectronics for low-energy information processing and communications,” Journal of Lightwave Technology 35
2017
Earlier work this paper cites.
B. Fahs, A. J. Chowdhury, Y. Zhang, J. Ghasemi, C. Hitchcock, P. Zarkesh-Ha, and M. M. Hella, “Design and modeling of blue-enhanced and bandwidth-extended PN photodiode in standard CMOS technology,” IEEE Transactions on Electron Devices 64
2017
Earlier work this paper cites.
C. Rosales-Guzmán and A. Forbes, How to shape light with spatial light modulators (SPIE Press, Bellingham, WA, USA, 2017)
2017
Earlier work this paper cites.
A. Stillmaker and B. Baas, “Scaling equations for the accurate prediction of CMOS device performance from 180 nm to 7 nm,” Integration 58
2017
Cited alongside, same era.
M. Shoba and R. Nakkeeran, “Energy and area efficient hierarchy multiplier architecture based on Vedic mathematics and GDI logic,” Engineering Science and Technology, an International Journal 20
2017
Cited alongside, same era.
B. Fahs, A. J. Chowdhury, Y. Zhang, J. Ghasemi, C. Hitchcock, P. Zarkesh-Ha, and M. M. Hella, “Design and modeling of blue-enhanced and bandwidth-extended PN photodiode in standard CMOS technology,” IEEE Transactions on Electron Devices 64
2017
Cited alongside, same era.
V. Sze, Y. Chen, T. Yang, and J. S. Emer, “Efficient processing of deep neural networks: A tutorial and survey,” Proc. IEEE 105
2017
Cited alongside, same era.
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. Le Scao, S. Gugger, M. Drame, Q. Lhoest, and A. Rush, “Transformers: State-of-the-art natural language processing,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, (Association for Computational Linguistics, Online, 2020), pp. 38–45
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Sebastian, M. Le Gallo, R. Khaddam-Aljameh, and E. Eleftheriou, “Memory devices and applications for in-memory computing,” Nature Nanotechnology 15
2020
Later among the works it cites.
P. Yao, H. Wu, B. Gao, J. Tang, Q. Zhang, W. Zhang, J. J. Yang, and H. Qian, “Fully hardware-implemented memristor convolutional neural network,” Nature 577
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers et al. , “In-datacenter performance analysis of a tensor processing unit,” in Proceedings of the 44th annual international symposium on computer architecture, (2017), pp. 1–12
2017
Cited alongside, same era.
L. Kull, D. Luu, C. Menolfi, M. Braendli, P. A. Francese, T. Morf, M. Kossel, H. Yueksel, A. Cevrero, I. Ozkaya, and T. Toifl, “A 10b 1.5 GS/s pipelined-SAR ADC with background second-stage common-mode regulation and offset calibration in 14nm CMOS FinFET,” in 2017 IEEE International Solid-State Circuits Conference (ISSCC), (IEEE, 2017), pp. 474–475
2017
Cited alongside, same era.
D. A. B. Miller, “Attojoule optoelectronics for low-energy information processing and communications,” Journal of Lightwave Technology 35
2017
Cited alongside, same era.
X. Xu, Y. Ding, S. X. Hu, M. Niemier, J. Cong, Y. Hu, and Y. Shi, “Scaling for edge inference of deep neural networks,” Nature Electronics 1
2018
Cited alongside, same era.
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
2018
Cited alongside, same era.
J. Chang, V. Sitzmann, X. Dun, W. Heidrich, and G. Wetzstein, “Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification,” Scientific reports 8
2018
Cited alongside, same era.
M. Kuramoto, S. Kobayashi, T. Akagi, K. Tazawa, K. Tanaka, T. Saito, and T. Takeuchi, “High-output-power and high-temperature operation of blue GaN-based vertical-cavity surface-emitting laser,” Applied Physics Express 11
2018
Cited alongside, same era.
H. Saadat, H. Bokhari, and S. Parameswaran, “Minimally biased multipliers for approximate integer and floating-point multiplication,” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 37
2018
Cited alongside, same era.
2020
Later among the works it cites.
T. P. Xiao, C. H. Bennett, B. Feinberg, S. Agarwal, and M. J. Marinella, “Analog architectures for neural network acceleration based on non-volatile memory,” Applied Physics Reviews 7
2020
Later among the works it cites.
T. P. Xiao, C. H. Bennett, B. Feinberg, S. Agarwal, and M. J. Marinella, “Analog architectures for neural network acceleration based on non-volatile memory,” Applied Physics Reviews 7
2020
Later among the works it cites.
J. Spall, X. Guo, T. D. Barrett, and A. Lvovsky, “Fully reconfigurable coherent optical vector–matrix multiplication,” Optics Letters 45
2020
Later among the works it cites.
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,” Optica 7
2020
Later among the works it cites.
2021
Later among the works it cites.
J. Feldmann, N. Youngblood, M. Karpov, H. Gehring, H. Li, M. Stappers, M. Le Gallo, X. Fu, A. Lukashchuk, A. S. Raja, J. Liu, C. D. Wright, A. Sebastian, T. J. Kippenberg, W. H. P. Pernice, and H. Bhaskaran, “Parallel convolutional processing using an integrated photonic tensor core,” Nature 589
2021
Later among the works it cites.
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,” Nature Photonics 15
2021
Later among the works it cites.
P. Kharel, C. Reimer, K. Luke, L. He, and M. Zhang, “Breaking voltage-bandwidth limits in integrated lithium niobate modulators using micro-structured electrodes,” Optica 8
2021
Later among the works it cites.
Y. Zhang, C. Fowler, J. Liang, B. Azhar, M. Y. Shalaginov, S. Deckoff-Jones, S. An, J. B. Chou, C. M. Roberts, V. Liberman, M. Kang, C. Rios, K. A. Richardson, C. Rivero-Baleine, T. Gu, H. Zhang, and J. Hu, “Electrically reconfigurable non-volatile metasurface using low-loss optical phase-change material,” Nature Nanotechnology 16
2021
Later among the works it cites.
L. Bernstein, A. Sludds, R. Hamerly, V. Sze, J. Emer, and D. Englund, “Freely scalable and reconfigurable optical hardware for deep learning,” Scientific Reports 11
2021
Later among the works it cites.
J. Degrave, F. Felici, J. Buchli, M. Neunert, B. Tracey, F. Carpanese, T. Ewalds, R. Hafner, A. Abdolmaleki, D. de Las Casas et al. , “Magnetic control of tokamak plasmas through deep reinforcement learning,” Nature 602
2022
Closest in time.
T. Wang, S.-Y. Ma, L. G. Wright, T. Onodera, B. C. Richard, and P. L. McMahon, “An optical neural network using less than 1 photon per multiplication,” Nature Communications 13
2022
Closest in time.
A. Skalli, J. Robertson, D. Owen-Newns, M. Hejda, X. Porte, S. Reitzenstein, A. Hurtado, and D. Brunner, “Photonic neuromorphic computing using vertical cavity semiconductor lasers,” Optical Materials Express 12
2022
Closest in time.
2022
Closest in time.
C. Han, M. Jin, Y. Tao, B. Shen, H. Shu, and X. Wang, “Ultra-compact silicon modulator with 110 GHz bandwidth,” in Optical Fiber Communication Conference, (Optica Publishing Group, 2022), pp. Th4C–5
2022
Closest in time.
Y. Jung, H. Han, A. Sharma, J. Jeong, S. S. Parkin, and J. K. Poon, “Integrated hybrid VO 2 –silicon optical memory,” ACS Photonics (2022)
2022
Closest in time.
O. Morales Chacón, J. J. Wikner, C. Svensson, L. Siek, and A. Alvandpour, “Analysis of energy consumption bounds in CMOS current-steering digital-to-analog converters,” Analog Integrated Circuits and Signal Processing pp. 1–13 (2022)
2022
Closest in time.
A. Skalli, J. Robertson, D. Owen-Newns, M. Hejda, X. Porte, S. Reitzenstein, A. Hurtado, and D. Brunner, “Photonic neuromorphic computing using vertical cavity semiconductor lasers,” Optical Materials Express 12
2022
Closest in time.
O. Morales Chacón, J. J. Wikner, C. Svensson, L. Siek, and A. Alvandpour, “Analysis of energy consumption bounds in CMOS current-steering digital-to-analog converters,” Analog Integrated Circuits and Signal Processing pp. 1–13 (2022)
2022
Closest in time.
HOLOEYE Photonics AG, “GAEA-2 10 megapixel phase only LCOS-SLM (reflective),” holoeye.com/gaea-4k-phase-only-spatial-light-modulator/ . Accessed: 2022-05-25
2022
Closest in time.
T. Wang, S.-Y. Ma, L. G. Wright, T. Onodera, B. C. Richard, and P. L. McMahon, “An optical neural network using less than 1 photon per multiplication,” Nature Communications 13
2022
Closest in time.