• Acta Photonica Sinica
  • Vol. 50, Issue 10, 1020001 (2021)
Shuiying XIANG1,2,*, Ziwei SONG1, Shuang GAO1, Yanan HAN1..., Yahui ZHANG1, Xingxing GUO1 and Yue HAO2|Show fewer author(s)
Author Affiliations
  • 1State Key Laboratory of Integrated Service Networks,Xidian University,Xi'an 710071,China
  • 2State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology,School of Microelectronics,Xidian University,Xi'an 710071,China
  • show less
    DOI: 10.3788/gzxb20215010.1020001 Cite this Article
    Shuiying XIANG, Ziwei SONG, Shuang GAO, Yanan HAN, Yahui ZHANG, Xingxing GUO, Yue HAO. Progress and Prospects of Photonic Neuromorphic Computing(Invited)[J]. Acta Photonica Sinica, 2021, 50(10): 1020001 Copy Citation Text show less
    References

    [1] G E MOORE. Cramming more components onto integrated circuits. Electronics, 38, 114(1965).

    [2] T R INSEL, S C LANDIS, F S COLLINS. The NIH brain initiative. Science, 340, 687-688(2013).

    [3] C L MARTIN, M CHUN. The BRAIN initiative: building, strengthening, and sustaining. Neuron, 92, 570-573(2016).

    [4] K AMUNTS, C EBELL, J MULLER et al. The human brain project: creating a European research infrastructure to decode the human brain. Neuron, 92, 574-581(2016).

    [5] H OKANO, E SASAKI, T YAMAMORI et al. Brain/MINDS: a Japanese national brain project for marmoset neuroscience. Neuron, 92, 582-590(2016).

    [6] POO Muming. Where to the mega brain projects?. National Science Review, 1, 12-14(2014).

    [7] POO Muming, Jiulin DU, N Y IP et al. China brain project: basic neuroscience, brain diseases, and brain-inspired computing. Neuron, 92, 591-596(2016).

    [8] POO Muming, Bo XU, Tieniu TAN. Brain science and brain-inspired intelligence technology-an overview. Bulletin of the Chinese Academy of Sciences, 31, 725-736(2016).

    [9] Tiejun HUANG, Luping SHI, Huajin TANG et al. Research on multimedia technology 2015-advances and trend of brain-like computing. Journal of Image and Graphics, 21, 1411-1424(2016).

    [10] C A MEAD. Analog VLSI and neural systems(1989).

    [11] B V BENJAMIN, Peiran GAO, E MCQUINN et al. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proceedings of the IEEE, 102, 699-716(2014).

    [12] E PAINKRAS, L A PLANA, J GARSIDE et al. SpiNNaker: a 1-W 18-core system-on-chip for massively-parallel neural network simulation. IEEE Journal of Solid-State Circuits, 48, 1943-1953(2013).

    [13] J SCHEMMEL, D BRUDERLE, A GRUBL et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling, 1947-1950(2010).

    [14] P A MEROLLA, J V ARTHUR, R ALVAREZ-ICAZA et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345, 668-673(2014).

    [15] M DAVIES, N SRINIVASA, T H LIN et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro, 38, 82-99(2018).

    [16] Juncheng SHEN, De MA, Zonghua GU et al. Darwin: a neuromorphic hardware co-processor based on spiking neural networks. Science China Information Sciences, 59, 1-5(2016).

    [17] Luping SHI, Jing PEI, Ning DENG et al. Development of a neuromorphic computing system, 4.3.1-4.3.4(2015).

    [18] M ANTHONY. Discrete mathematics of neural networks: selected topics(2001).

    [19] W MAASS. Networks of spiking neurons: The third generation of neural network models. Neural Networks, 10, 1659-1671(1997).

    [20] J GEORGE, R AMIN, A MEHRABIAN et al. Electrooptic nonlinear activation functions for vector matrix multiplications in optical neural networks, SpW4G. 3(2018).

    [21] J K GEORGE, A MEHRABIAN, R AMIN et al. Neuromorphic photonics with electro-absorption modulators. Optics Express, 27, 5181-5191(2019).

    [22] A N TAIT, T F DE LIMA, M A NAHMIAS et al. Silicon photonic modulator neuron. Physical Review Applied, 11, 064043(2019).

    [23] A JHA, Chaoran HUANG, P R PRUCNAL. Reconfigurable all-optical nonlinear activation functions for neuromorphic photonics. Optics Letters, 45, 4819-4822(2020).

    [24] I A D WILLIAMSON, T W HUGHES, M MINKOV et al. Reprogrammable electro-optic nonlinear activation functions for optical neural networks. IEEE Journal of Selected Topics in Quantum Electronics, 26, 1-12(2019).

    [25] M M P FARD, I A D WILLIAMSON, M EDWARDS et al. Experimental realization of arbitrary activation functions for optical neural networks. Optics Express, 28, 12138-12148(2020).

    [26] G MOURGIAS-ALEXANDRIS, A TSAKYRIDIS, N PASSALIS et al. An all-optical neuron with sigmoid activation function. Optics Express, 27, 9620-9630(2019).

    [27] Bin SHI, K PRIFTI, E MAGALHAES et al. Lossless monolithically integrated photonic InP neuron for all-optical computation, W2A. 12(2020).

    [28] P R PRUCNAL, B J SHASTRI, T F DE LIMA et al. Recent progress in semiconductor excitable lasers for photonic spike processing. Advances in Optics and Photonics, 8, 228-299(2016).

    [29] M A NAHMIAS, B J SHASTRI, A N TAIT et al. A leaky integrate-and-fire laser neuron for ultrafast cognitive computing. IEEE Journal of Selected Topics in Quantum Electronics, 19, 1-12(2013).

    [30] M A NAHMIAS, A N TAIT, B J SHASTRI et al. Excitable laser processing network node in hybrid silicon: analysis and simulation. Optics Express, 23, 26800-26813(2015).

    [31] H T PENG, M A NAHMIAS, T F DE LIMA et al. Neuromorphic photonic integrated circuits. IEEE Journal of Selected Topics in Quantum Electronics, 24, 1-15(2018).

    [32] H T PENG, G ANGELATOS, T F DE LIMA et al. Temporal information processing with an integrated laser neuron. IEEE Journal of Selected Topics in Quantum Electronics, 26, 1-9(2019).

    [33] B J SHASTRI, M A NAHMIAS, A N TAIT et al. Simulations of a graphene excitable laser for spike processing. Optical and Quantum Electronics, 46, 1353-1358(2014).

    [34] B J SHASTRI, M A NAHMIAS, A N TAIT et al. Spike processing with a graphene excitable laser. Scientific Reports, 6, 1-12(2016).

    [35] P Y MA, B J SHASTRI, T F DE LIMA et al. Simultaneous excitatory and inhibitory dynamics in an excitable laser. Optics Letters, 43, 3802-3805(2018).

    [36] P Y MA, B J SHASTRI, T F DE LIMA et al. All-optical digital-to-spike conversion using a graphene excitable laser. Optics Express, 25, 33504-33513(2017).

    [37] A HURTADO, I D HENNING, M J ADAMS. Optical neuron using polarisation switching in a 1550nm-VCSEL. Optics Express, 18, 25170-25176(2010).

    [38] A HURTADO, K SCHIRES, I D HENNING et al. Investigation of vertical cavity surface emitting laser dynamics for neuromorphic photonic systems. Applied Physics Letters, 100, 103703(2012).

    [39] J ROBERTSON, T DENG, J JAVALOYES et al. Controlled inhibition of spiking dynamics in VCSELs for neuromorphic photonics: theory and experiments. Optics Letters, 42, 1560-1563(2017).

    [40] Tao DENG, J ROBERTSON, A HURTADO. Controlled propagation of spiking dynamics in vertical-cavity surface-emitting lasers: towards neuromorphic photonic networks. IEEE Journal of Selected Topics in Quantum Electronics, 23, 1-8(2017).

    [41] J ROBERTSON, E WADE, A HURTADO. Electrically controlled neuron-like spiking regimes in vertical-cavity surface-emitting lasers at ultrafast rates. IEEE Journal of Selected Topics in Quantum Electronics, 25, 1-7(2019).

    [42] J ROBERTSON, M HEJDA, J BUENO et al. Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons. Scientific Reports, 10, 1-8(2020).

    [43] J ROBERTSON, E WADE, Y KOPP et al. Toward neuromorphic photonic networks of ultrafast spiking laser neurons. IEEE Journal of Selected Topics in Quantum Electronics, 26, 1-15(2019).

    [44] B KELLEHER, D GOULDING, S P HEGARTY et al. Excitable phase slips in an injection-locked single-mode quantum-dot laser. Optics Letters, 34, 440-442(2009).

    [45] C MESARITAKIS, A KAPSALIS, A BOGRIS et al. Artificial neuron based on integrated semiconductor quantum dot mode-locked lasers. Scientific Reports, 6, 1-10(2016).

    [46] G SARANTOGLOU, M SKONTRANIS, C MESARITAKIS. All optical integrate and fire neuromorphic node based on single section quantum dot laser. IEEE Journal of Selected Topics in Quantum Electronics, 26, 1-10(2020).

    [47] W COOMANS, L GELENS, S BERI et al. Solitary and coupled semiconductor ring lasers as optical spiking neurons. Physical Review E: Statistical, 84, 036209(2011).

    [48] K ALEXANDER, VAERENBERGH TVAN, M FIERS et al. Excitability in optically injected microdisk lasers with phase controlled excitatory and inhibitory response. Optics Express, 21, 26182-26191(2013).

    [49] F SELMI, R BRAIVE, G BEAUDOIN et al. Relative refractory period in an excitable semiconductor laser. Physical Review Letters, 112, 183902(2014).

    [50] F SELMI, R BRAIVE, G BEAUDOIN et al. Temporal summation in a neuromimetic micropillar laser. Optics Letters, 40, 5690-5693(2015).

    [51] F SELMI, R BRAIVE, G BEAUDOIN et al. Spike latency and response properties of an excitable micropillar laser. Physical Review E, 94, 042219(2016).

    [52] V A PAMMI, K ALFARO-BITTNER, M G CLERC et al. Photonic computing with single and coupled spiking micropillar lasers. IEEE Journal of Selected Topics in Quantum Electronics, 26, 1-7(2019).

    [53] I CHAKRABORTY, G SAHA, A SENGUPTA et al. Toward fast neural computing using all-photonic phase change spiking neurons. Scientific Reports, 8, 1-9(2018).

    [54] I CHAKRABORTY, G SAHA, K ROY. Photonic in-memory computing primitive for spiking neural networks using phase-change materials. Physical Review Applied, 11, 014063(2019).

    [55] A N TAIT, M A NAHMIAS, B J SHASTRI et al. Broadcast and weight: an integrated network for scalable photonic spike processing. Journal of Lightwave Technology, 32, 4029-4041(2014).

    [56] A N TAIT, J CHANG, B J SHASTRI et al. Demonstration of WDM weighted addition for principal component analysis. Optics Express, 23, 12758-12765(2015).

    [57] A N TAIT, T F DE LIMA, M A NAHMIAS et al. Continuous calibration of microring weights for analog optical networks. IEEE Photonics Technology Letters, 28, 887-890(2016).

    [58] A N TAIT, A X WU, T F DE LIMA et al. Microring weight banks. IEEE Journal of Selected Topics in Quantum Electronics, 22, 312-325(2016).

    [59] A N TAIT, T F DE LIMA, E ZHOU et al. Neuromorphic photonic networks using silicon photonic weight banks. Scientific Reports, 7, 1-10(2017).

    [60] A N TAIT, A X WU, T F DE LIMA et al. Two-pole microring weight banks. Optics Letters, 43, 2276-2279(2018).

    [61] P Y MA, A N TAIT, T F DE LIMA et al. Photonic independent component analysis using an on-chip microring weight bank. Optics Express, 28, 1827-1844(2020).

    [62] V BANGARI, B A MARQUEZ, H MILLER et al. Digital electronics and analog photonics for convolutional neural networks (DEAP-CNNs). IEEE Journal of Selected Topics in Quantum Electronics, 26, 1-13(2020).

    [63] Chaoran HUANG, S BILODEAU, T F DE LIMA et al. Demonstration of scalable microring weight bank control for large-scale photonic integrated circuits. APL Photonics, 5, 040803(2020).

    [64] Weipeng ZHANG, Chaoran HUANG, S BILODEAU et al. Microring weight banks control beyond 8.5-bits accuracy. arXiv preprint arXiv(2021).

    [65] S OHNO, K TOPRASERTPONG, S TAKAGI et al. Si microring resonator crossbar array for on-chip inference and training of optical neural network. arXiv preprint arXiv(2021).

    [66] M J FILIPOVICH, Zhimu GUO, B A MARQUEZ et al. Training deep neural networks in situ with neuromorphic photonics, 1-2(2020).

    [67] F SUNNY, A MIRZA, M NIKDAST et al. CrossLight: a cross-layer optimized silicon photonic neural network accelerator. arXiv preprint arXiv(2021).

    [68] M RECK, A ZEILINGER, H J BERNSTEIN et al. Experimental realization of any discrete unitary operator. Physical Review Letters, 73, 58(1994).

    [69] W R CLEMENTS, P C HUMPHREYS, B J METCALF et al. Optimal design for universal multiport interferometers. Optica, 3, 1460-1465(2016).

    [70] Yichen SHEN, N C HARRIS, S SKIRLO et al. Deep learning with coherent nanophotonic circuits. Nature Photonics, 11, 441-446(2017).

    [71] J K GEORGE, H NEJADRIAHI, V J SORGER. Towards on-chip optical FFTs for convolutional neural networks, 1-4(2017).

    [72] M Y S FANG, S MANIPATRUNI, C WIERZYNSKI et al. Design of optical neural networks with component imprecisions. Optics Express, 27, 14009-14029(2019).

    [73] F SHOKRANEH, S GEOFFROY-GAGNON, O LIBOIRON-LADOUCEUR. The diamond mesh, a phase-error-and loss-tolerant field-programmable MZI-based optical processor for optical neural networks. Optics Express, 28, 23495-23508(2020).

    [74] F SHOKRANEH, S GEOFFROY-GAGNON, O LIBOIRON-LADOUCEUR. Towards phase-error-and loss-tolerant programmable MZI-based optical processors for optical neural networks, 1-2(2020).

    [75] M P FOK, Y TIAN, D ROSENBLUTH et al. Pulse lead/lag timing detection for adaptive feedback and control based on optical spike-timing-dependent plasticity. Optics Letters, 38, 419-421(2013).

    [76] R TOOLE, M P FOK. Photonic implementation of a neuronal learning algorithm based on spike timing dependent plasticity, W1K. 6(2015).

    [77] R TOOLE, M P FOK. Photonic implementation of a neuronal algorithm applicable towards angle of arrival detection and localization. Optics Express, 23, 16133-16141(2015).

    [78] R TOOLE, A N TAIT, T F DE LIMA et al. Photonic implementation of spike-timing-dependent plasticity and learning algorithms of biological neural systems. Journal of Lightwave Technology, 34, 470-476(2015).

    [79] Z CHENG, C RIOS, W H P PERNICE et al. On-chip photonic synapse. Science Advances, 3, e1700160(2017).

    [80] J FELDMANN, N YOUNGBLOOD, C D WRIGHT et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature, 569, 208-214(2019).

    [81] K VANDOORNE, P MECHET, VAERENBERGH TVAN et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nature Communications, 5, 1-6(2014).

    [82] Xing LIN, Y RIVENSON, N T YARDIMCI et al. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).

    [83] Ying ZUO, Bohan LI, Yujun ZHAO et al. All-optical neural network with nonlinear activation functions. Optica, 6, 1132-1137(2019).

    [84] R HAMERLY, L BERNSTEIN, A SLUDDS et al. Large-scale optical neural networks based on photoelectric multiplication. Physical Review X, 9, 021032(2019).

    [85] M RAFAYELYAN, J DONG, Y Tan et al. Large-scale optical reservoir computing for spatiotemporal chaotic systems prediction. Physical Review X, 10, 041037(2020).

    [86] Bin SHI, N CALABRETTA, R STABILE. Deep neural network through an InP SOA-based photonic integrated cross-connect. IEEE Journal of Selected Topics in Quantum Electronics, 26, 1-11(2019).

    [87] J FELDMANN, N YOUNGBLOOD, M KARPOV et al. Parallel convolutional processing using an integrated photonic tensor core. Nature, 589, 52-58(2021).

    [88] Xingyuan XU, Mengxi TAN, B CORCORAN et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature, 589, 44-51(2021).

    [89] Quansheng REN, Yaolin ZHANG, Rui WANG et al. Optical spike-timing-dependent plasticity with weight-dependent learning window and reward modulation. Optics Express, 23, 25247-25258(2015).

    [90] Yaolin ZHANG, Quansheng REN, Jianye ZHAO. Implementation of optical multiplicative spike-timing-dependent plasticity with adaptive current feedback of semiconductor optical amplifiers, 1-2(2015).

    [91] Rui WANG, Cheng QIAN, Quansheng REN et al. Optoelectronic neuromorphic system using the neural engineering framework. Applied Optics, 56, 1517-1525(2017).

    [92] Qiang LI, Zhi WANG, Yansi LE et al. Optical implementation of neural learning algorithms based on cross-gain modulation in a semiconductor optical amplifier, 10019, 100190E(2016).

    [93] Qiang LI, Zhi WANG, Can CUI et al. Optical implementation of anti-spike-timing-dependent plasticity learning mechanism. Chinese Journal of Lasers, 44, 0508002(2017).

    [94] Qiang LI, Zhi WANG, Can CUI et al. Simulating the spiking response of VCSEL-based optical spiking neuron. Optics Communications, 407, 327-332(2018).

    [95] Tao DENG, J ROBERTSON, Z M WU et al. Stable propagation of inhibited spiking dynamics in vertical-cavity surface-emitting lasers for neuromorphic photonic networks. IEEE Access, 6, 67951-67958(2018).

    [96] Hua TAN, Zhenyi NI, Wenbing PENG et al. Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing. Nano Energy, 52, 422-430(2018).

    [97] Tian ZHANG, Jia WANG, Yihang DAN et al. Efficient training and design of photonic neural network through neuroevolution. Optics Express, 27, 37150-37163(2019).

    [98] Bowen MA, Weiwen ZOU. Demonstration of a distributed feedback laser diode working as a graded-potential-signaling photonic neuron and its application to neuromorphic information processing. Science China Information Sciences, 63, 1-8(2020).

    [99] Bowen MA, Jianping CHEN, Weiwen ZOU. A DFB-LD-based photonic neuromorphic network for spatiotemporal pattern recognition, M2K. 2(2020).

    [100] Shaofu XU, Jing WANG, Weiwen ZOU. Optical convolutional neural network with WDM-based optical patching and microring weighting banks. IEEE Photonics Technology Letters, 33, 89-92(2020).

    [101] Hailong ZHOU, Yuhe ZHAO, Gaoxiang XU et al. Chip-scale optical matrix computation for PageRank algorithm. IEEE Journal of Selected Topics in Quantum Electronics, 26, 8300910(2020).

    [102] Yuhe ZHAO, Hailong ZHOU, Jianji DONG. An optical processor for matrix computation on silicon-on-insulator, 1-3(2019).

    [103] Hailong ZHOU, Yuhe ZHAO, Xu WANG et al. Self-configuring and reconfigurable silicon photonic signal processor. ACS Photonics, 7, 792-799(2020).

    [104] Ruiting WANG, Pengfei WANG, Guangzhen LUO et al. Silicon-based optical neural network chip based on coherent detection, T2D. 4(2020).

    [105] Yubin ZANG, Minghua CHEN, Sigang YANG et al. Electro-optical neural networks based on time-stretch method. IEEE Journal of Selected Topics in Quantum Electronics, 26, 1-10(2019).

    [106] Yubin ZANG, Minghua CHEN, Sigang YANG et al. Optoelectronic convolutional neural networks based on time-stretch method. Science China Information Sciences, 64, 1-12(2021).

    [107] Tiankuang ZHOU, Xing LIN, Jiamin WU et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nature Photonics, 15, 367-373(2021).

    [108] E GOI, Xi CHEN, Qiming ZHANG et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light: Science & Applications, 10, 1-11(2021).

    [109] Yue HAO, Shuiying XIANG, Genquan HAN et al. Recent progress of integrated circuits and optoelectronic chips. Science China Information Sciences, 64, 1-33(2021).

    [110] Shuiying XIANG, Yanan HAN, Ziwei SONG et al. A review: photonics devices, architectures, and algorithms for optical neural computing. Journal of Semiconductors, 42, 023105(2021).

    [111] Shuiying XIANG, Aijun WEN, Wei PAN. Emulation of spiking response and spiking frequency property in VCSEL-based photonic neuron. IEEE Photonics Journal, 8, 1-9(2016).

    [112] Shuiying XIANG, Hao ZHANG, Xingxing GUO et al. Cascadable neuron-like spiking dynamics in coupled VCSELs subject to orthogonally polarized optical pulse injection. IEEE Journal of Selected Topics in Quantum Electronics, 23, 1-7(2017).

    [113] Shuiying XIANG, Yahui ZHANG, Xingxing GUO et al. Photonic generation of neuron-like dynamics using VCSELs subject to double polarized optical injection. Journal of Lightwave Technology, 36, 4227-4234(2018).

    [114] Yahui ZHANG, Shuiying XIANG, Junkai GONG et al. Spike encoding and storage properties in mutually coupled vertical-cavity surface-emitting lasers subject to optical pulse injection. Applied Optics, 57, 1731-1737(2018).

    [115] Yahui ZHANG, Shuiying XIANG, Xingxing GUO et al. All-optical inhibitory dynamics in photonic neuron based on polarization mode competition in a VCSEL with an embedded saturable absorber. Optics Letters, 44, 1548-1551(2019).

    [116] Shuiying XIANG, Zhenxing REN, Yahui ZHANG et al. All-optical neuromorphic XOR operation with inhibitory dynamics of a single photonic spiking neuron based on a VCSEL-SA. Optics Letters, 45, 1104-1107(2020).

    [117] Shuiying XIANG, Junkai GONG, Yahui ZHANG et al. Numerical implementation of wavelength-dependent photonic spike timing dependent plasticity based on VCSOA. IEEE Journal of Quantum Electronics, 54, 1-7(2018).

    [118] Shuiying XIANG, Yanan HAN, Xingxing GUO et al. Real-time optical spike-timing dependent plasticity in a single VCSEL with dual-polarized pulsed optical injection. Science China Information Sciences, 63, 1-12(2020).

    [119] Shuiying XIANG, Yahui ZHANG, Junkai GONG et al. STDP-based unsupervised spike pattern learning in a photonic spiking neural network with VCSELs and VCSOAs. IEEE Journal of Selected Topics in Quantum Electronics, 25, 1-9(2019).

    [120] Ziwei SONG, Shuiying XIANG, Zhenxing REN et al. Spike sequence learning in a photonic spiking neural network consisting of VCSELs-SA with supervised training. IEEE Journal of Selected Topics in Quantum Electronics, 26, 1-9(2020).

    [121] Yahui ZHANG, Shuiying XIANG, Xingxing GUO et al. The winner-take-all mechanism for all-optical systems of pattern recognition and max-pooling operation. Journal of Lightwave Technology, 38, 5071-5077(2020).

    [122] Yanan HAN, Shuiying XIANG, Zhenxing REN et al. Delay-weight plasticity-based supervised learning in optical spiking neural networks. Photonics Research, 9, B119-B127(2021).

    [123] Ziwei SONG, Shuiying XIANG, Zhenxing REN et al. Photonic spiking neural network based on excitable VCSELs-SA for sound azimuth detection. Optics Express, 28, 1561-1573(2020).

    [124] Suhong WANG, Shuiying XIANG, Genquan HAN et al. Photonic associative learning neural network based on VCSELs and STDP. Journal of Lightwave Technology, 38, 4691-4698(2020).

    [125] Shuiying XIANG, Zhenxing REN, Ziwei SONG et al. Computing primitive of fully VCSEL-based all-optical spiking neural network for supervised learning and pattern classification. IEEE Transactions on Neural Networks and Learning Systems, 32, 2494-2505(2020).

    [126] Xingxing GUO, Shuiying XIANG, Yahui ZHANG et al. Four-channels reservoir computing based on polarization dynamics in mutually coupled VCSELs system. Optics Express, 27, 23293-23306(2019).

    [127] Xingxing GUO, Shuiying XIANG, Yahui ZHANG et al. Polarization multiplexing reservoir computing based on a VCSEL with polarized optical feedback. IEEE Journal of Selected Topics in Quantum Electronics, 26, 1700109(2020).

    [128] Xingxing GUO, Shuiying XAING, Yahui ZHANG et al. Enhanced memory capacity of a neuromorphic reservoir computing system based on a VCSEL with double optical feedbacks. Science China Information Sciences, 63, 160407(2020).

    [129] Xingxing GUO, Shuiying XIANG, Yan QU et al. Enhanced prediction performance of a neuromorphic reservoir computing system using a semiconductor nanolaser with double phase conjugate feedbacks. Journal of Lightwave Technology, 39, 129-135(2021).

    [130] Yahui ZHANG, J ROBERTSON, Shuiying XIANG et al. All-optical neuromorphic binary convolution with a spiking VCSEL neuron for image gradient magnitudes. Photonics Research, 9, B201-B209(2021).

    Shuiying XIANG, Ziwei SONG, Shuang GAO, Yanan HAN, Yahui ZHANG, Xingxing GUO, Yue HAO. Progress and Prospects of Photonic Neuromorphic Computing(Invited)[J]. Acta Photonica Sinica, 2021, 50(10): 1020001
    Download Citation