BP-based supervised learning algorithm for multilayer photonic spiking neural network and hardware implementation

We introduce a supervised learning algorithm for photonic spiking neural network (SNN) based on back propagation. For the supervised learning algorithm, the information is encoded into spike trains with different strength, and the SNN is trained according to different patterns composed of different...

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Published in:	Optics express Vol. 31; no. 10; p. 16549
Main Authors:	Zhang, Yahui, Xiang, Shuiying, Han, Yanan, Guo, Xingxing, Zhang, Wu, Tan, Qinggui, Han, Genquan, Hao, Yue
Format:	Journal Article
Language:	English
Published:	United States 08.05.2023
ISSN:	1094-4087, 1094-4087
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Abstract	We introduce a supervised learning algorithm for photonic spiking neural network (SNN) based on back propagation. For the supervised learning algorithm, the information is encoded into spike trains with different strength, and the SNN is trained according to different patterns composed of different spike numbers of the output neurons. Furthermore, the classification task is performed numerically and experimentally based on the supervised learning algorithm in the SNN. The SNN is composed of photonic spiking neuron based on vertical-cavity surface-emitting laser which is functionally similar to leaky-integrate and fire neuron. The results prove the demonstration of the algorithm implementation on hardware. To seek ultra-low power consumption and ultra-low delay, it is great significance to design and implement a hardware-friendly learning algorithm of photonic neural networks and realize hardware-algorithm collaborative computing.
AbstractList	We introduce a supervised learning algorithm for photonic spiking neural network (SNN) based on back propagation. For the supervised learning algorithm, the information is encoded into spike trains with different strength, and the SNN is trained according to different patterns composed of different spike numbers of the output neurons. Furthermore, the classification task is performed numerically and experimentally based on the supervised learning algorithm in the SNN. The SNN is composed of photonic spiking neuron based on vertical-cavity surface-emitting laser which is functionally similar to leaky-integrate and fire neuron. The results prove the demonstration of the algorithm implementation on hardware. To seek ultra-low power consumption and ultra-low delay, it is great significance to design and implement a hardware-friendly learning algorithm of photonic neural networks and realize hardware-algorithm collaborative computing. We introduce a supervised learning algorithm for photonic spiking neural network (SNN) based on back propagation. For the supervised learning algorithm, the information is encoded into spike trains with different strength, and the SNN is trained according to different patterns composed of different spike numbers of the output neurons. Furthermore, the classification task is performed numerically and experimentally based on the supervised learning algorithm in the SNN. The SNN is composed of photonic spiking neuron based on vertical-cavity surface-emitting laser which is functionally similar to leaky-integrate and fire neuron. The results prove the demonstration of the algorithm implementation on hardware. To seek ultra-low power consumption and ultra-low delay, it is great significance to design and implement a hardware-friendly learning algorithm of photonic neural networks and realize hardware-algorithm collaborative computing.We introduce a supervised learning algorithm for photonic spiking neural network (SNN) based on back propagation. For the supervised learning algorithm, the information is encoded into spike trains with different strength, and the SNN is trained according to different patterns composed of different spike numbers of the output neurons. Furthermore, the classification task is performed numerically and experimentally based on the supervised learning algorithm in the SNN. The SNN is composed of photonic spiking neuron based on vertical-cavity surface-emitting laser which is functionally similar to leaky-integrate and fire neuron. The results prove the demonstration of the algorithm implementation on hardware. To seek ultra-low power consumption and ultra-low delay, it is great significance to design and implement a hardware-friendly learning algorithm of photonic neural networks and realize hardware-algorithm collaborative computing.
Author	Han, Yanan Zhang, Wu Han, Genquan Guo, Xingxing Zhang, Yahui Tan, Qinggui Hao, Yue Xiang, Shuiying
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References	Zhang (oe-31-10-16549-R9) 2021; 64 Vladimirov (oe-31-10-16549-R33) 2005; 72 Merolla (oe-31-10-16549-R16) 2014; 345 Wu (oe-31-10-16549-R25) 2023; 29 Nahmias (oe-31-10-16549-R38) 2013; 19 Chlouverakis (oe-31-10-16549-R32) 2004; 10 oe-31-10-16549-R19 Fu (oe-31-10-16549-R30) 2022; 30 Moradi (oe-31-10-16549-R18) 2018; 12 Jha (oe-31-10-16549-R31) 2022; 40 Deng (oe-31-10-16549-R34) 2017; 23 Xiang (oe-31-10-16549-R40) 2023; 10 Arsalan (oe-31-10-16549-R5) 2022; 1 Lian (oe-31-10-16549-R26) 2022; 43 Ponulak (oe-31-10-16549-R6) 2010; 22 Chen (oe-31-10-16549-R43) 2022; 1 Xiang (oe-31-10-16549-R46) 2021; 32 Deng (oe-31-10-16549-R37) 2018; 6 Xiao (oe-31-10-16549-R1) 2023; 31 Lee (oe-31-10-16549-R8) 2019; 11 Xiang (oe-31-10-16549-R35) 2017; 23 Balaji (oe-31-10-16549-R4) 2020; 13 Filipovich (oe-31-10-16549-R22) 2022; 9 Dubbeldam (oe-31-10-16549-R39) 1999; 159 Han (oe-31-10-16549-R45) 2021; 9 Xiang (oe-31-10-16549-R13) 2022; 11 Gütig (oe-31-10-16549-R7) 2006; 9 Pammi (oe-31-10-16549-R42) 2020; 26 Shi (oe-31-10-16549-R24) 2023; 29 Davies (oe-31-10-16549-R17) 2018; 38 Liu (oe-31-10-16549-R27) 2021 oe-31-10-16549-R23 Willemsen (oe-31-10-16549-R36) 2000; 77 Rumelhart (oe-31-10-16549-R47) 1986; 323 Neftci (oe-31-10-16549-R15) 2019; 36 Brückerhoff-Plückelmann (oe-31-10-16549-R20) 2023; 12 Feldmann (oe-31-10-16549-R44) 2019; 569
References_xml	– volume: 64 start-page: 122403 year: 2021 ident: oe-31-10-16549-R9 publication-title: Sci. China Inf. Sci. doi: 10.1007/s11432-020-3040-1 – volume: 23 start-page: 1 year: 2017 ident: oe-31-10-16549-R35 publication-title: IEEE J. Select. Topics Quantum Electron. doi: 10.1109/JSTQE.2017.2678170 – volume: 569 start-page: 208 year: 2019 ident: oe-31-10-16549-R44 publication-title: Nature doi: 10.1038/s41586-019-1157-8 – ident: oe-31-10-16549-R19 doi: 10.1109/IPC53466.2022.9975676 – volume: 72 start-page: 033808 year: 2005 ident: oe-31-10-16549-R33 publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.72.033808 – volume: 345 start-page: 668 year: 2014 ident: oe-31-10-16549-R16 publication-title: Science doi: 10.1126/science.1254642 – volume: 159 start-page: 325 year: 1999 ident: oe-31-10-16549-R39 publication-title: Opt. Commun. doi: 10.1016/S0030-4018(98)00568-9 – volume: 1 start-page: 1 year: 2022 ident: oe-31-10-16549-R5 publication-title: Appl. Intell. doi: 10.1007/s10489-022-04258-w – volume: 323 start-page: 533 year: 1986 ident: oe-31-10-16549-R47 publication-title: Nature doi: 10.1038/323533a0 – volume: 9 start-page: 420 year: 2006 ident: oe-31-10-16549-R7 publication-title: Nat. Neurosci. doi: 10.1038/nn1643 – volume: 9 start-page: B119 year: 2021 ident: oe-31-10-16549-R45 publication-title: Photonics Res. doi: 10.1364/PRJ.413742 – volume: 30 start-page: 44943 year: 2022 ident: oe-31-10-16549-R30 publication-title: Opt. Express doi: 10.1364/OE.476110 – volume: 43 start-page: 2192 year: 2022 ident: oe-31-10-16549-R26 publication-title: IEEE Electron Device Lett. doi: 10.1109/LED.2022.3218626 – volume: 40 start-page: 2901 year: 2022 ident: oe-31-10-16549-R31 publication-title: J. Lightwave Technol. doi: 10.1109/JLT.2022.3146157 – volume: 6 start-page: 67951 year: 2018 ident: oe-31-10-16549-R37 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2878940 – volume: 31 start-page: 439 year: 2023 ident: oe-31-10-16549-R1 publication-title: IEEE/ACM Trans. Audio Speech Lang. Process. doi: 10.1109/TASLP.2022.3221011 – volume: 77 start-page: 3514 year: 2000 ident: oe-31-10-16549-R36 publication-title: Appl. Phys. Lett. doi: 10.1063/1.1330217 – volume: 12 start-page: 819 year: 2023 ident: oe-31-10-16549-R20 publication-title: Nanophotonics doi: 10.1515/nanoph-2022-0441 – volume: 32 start-page: 2494 year: 2021 ident: oe-31-10-16549-R46 publication-title: IEEE Trans. Neural Netw. Learning Syst. doi: 10.1109/TNNLS.2020.3006263 – volume: 26 start-page: 1 year: 2020 ident: oe-31-10-16549-R42 publication-title: IEEE J. Select. Topics Quantum Electron. doi: 10.1109/JSTQE.2019.2929187 – volume: 36 start-page: 51 year: 2019 ident: oe-31-10-16549-R15 publication-title: IEEE Signal Process. Mag. doi: 10.1109/MSP.2019.2931595 – year: 2021 ident: oe-31-10-16549-R27 article-title: An energy-efficient non-volatile silicon photonic accelerator for convolutional neural networks (NVSP-CNNs) doi: 10.1364/ACPC.2021.T4A.244 – volume: 23 start-page: 1 year: 2017 ident: oe-31-10-16549-R34 publication-title: IEEE J. Select. Topics Quantum Electron. doi: 10.1109/JSTQE.2017.2685140 – volume: 38 start-page: 82 year: 2018 ident: oe-31-10-16549-R17 publication-title: IEEE Micro doi: 10.1109/MM.2018.112130359 – volume: 29 start-page: 1 year: 2023 ident: oe-31-10-16549-R24 publication-title: IEEE J. Select. Topics Quantum Electron. doi: 10.1109/JSTQE.2022.3226138 – volume: 11 start-page: 2097 year: 2022 ident: oe-31-10-16549-R13 publication-title: Electronics doi: 10.3390/electronics11132097 – ident: oe-31-10-16549-R23 doi: 10.34133/icomputing.0031 – volume: 10 start-page: 162 year: 2023 ident: oe-31-10-16549-R40 publication-title: Optica doi: 10.1364/OPTICA.468347 – volume: 12 start-page: 106 year: 2018 ident: oe-31-10-16549-R18 publication-title: IEEE Trans. Biomed. Circuits Syst. doi: 10.1109/TBCAS.2017.2759700 – volume: 9 start-page: 1323 year: 2022 ident: oe-31-10-16549-R22 publication-title: Optica doi: 10.1364/OPTICA.475493 – volume: 13 start-page: 142 year: 2020 ident: oe-31-10-16549-R4 publication-title: IEEE Comput. Archit. Lett. doi: 10.1109/LES.2020.3025873 – volume: 22 start-page: 467 year: 2010 ident: oe-31-10-16549-R6 publication-title: Neural Comput. doi: 10.1162/neco.2009.11-08-901 – volume: 1 start-page: 1859 year: 2022 ident: oe-31-10-16549-R43 publication-title: Opt. Commun. doi: 10.1364/OPTCON.461448 – volume: 10 start-page: 982 year: 2004 ident: oe-31-10-16549-R32 publication-title: IEEE J. Select. Topics Quantum Electron. doi: 10.1109/JSTQE.2004.837012 – volume: 11 start-page: 384 year: 2019 ident: oe-31-10-16549-R8 publication-title: IEEE Trans. Cogn. Dev. Syst. doi: 10.1109/TCDS.2018.2833071 – volume: 29 start-page: 1 year: 2023 ident: oe-31-10-16549-R25 publication-title: IEEE J. Select. Topics Quantum Electron. doi: 10.1109/JSTQE.2022.3217819 – volume: 19 start-page: 1 year: 2013 ident: oe-31-10-16549-R38 publication-title: IEEE J. Select. Topics Quantum Electron. doi: 10.1109/JSTQE.2013.2257700
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Snippet	We introduce a supervised learning algorithm for photonic spiking neural network (SNN) based on back propagation. For the supervised learning algorithm, the...
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