Kotelnikov Institute of Radio Engineering and Electronics of RAS Русский Sign in Submit a paper
Journal of Radio Electronics Online scientific journal · eISSN 1684-1719
2026, № 2 · RADIO ELECTRONICS RELATED PROBLEMS

Photonic technologies for implementing neuromorphic computing: results, research directions, and AI hardware

A.A. Machnev1,2, I.V. Melnikov2, E.R. Nadezhdin2, V.A. Sokolov2, D.D. Stupin2

1 MISIS University of Science and Technology, Moscow, Russia
2 Moscow Institute of Physics and Technology, Dolgoprudnyi
Full text DOI: 10.30898/1684-1719.2026.2.12 UDC 621.383 EDN VFWHSY

Abstract

Making use of photonic technologies significantly transforms an entire area of high-performance computing by offering new generation of ultra-fast computing systems, overcoming limitations pertinent to traditional electronic hardware in terms of energy efficiency, parallelism, and scalability. Such unique properties of light as an ensured high throughput, low time delay, and capability of parallelizing large data arrays and streams, photonic integrated circuits (PICs) offer a promising alternative for performing complex computations having a direct impact on artificial intelligence (AI) development. This review summarizes recent advances in photonic neuromorphic architectures (analog optical computing, photonic tank computing), and provides some insight into future research.

  • photonics
  • Photonic Technology
  • high performance computing
  • neuromorphic computing
  • artificial intelligence
  • photonic integrated circuits
  • parallel computing
  • neural networks
Аннотация (на русском)

Фотонные технологии для реализации нейроморфных вычислений: результаты, направления исследований и аппаратное обеспечение ИИ

Внедрение фотонных технологий в индустрию высокопроизводительных вычислений позволяет серьезно трансформировать подходы к созданию новых поколений сверхбыстродействующих вычислительных систем, преодолевая присущие традиционным электронным вычислительным системам и технологиям ограничения с точки зрения энергоэффективности, распараллеливания и масштабируемости. Используя уникальные свойства светового излучения с точки зрения обеспечения высокой пропускной способности, малых временных задержек и возможности параллельной обработки больших информационных массивов и потоков, фотонные вычислительные системы представляют собой многообещающую альтернативу для выполнения сложных вычислительных операций, в том числе, в приложениях, предполагающих использование технологий искусственного интеллекта. В представленной работе синтезируются последние достижения в области фотонных нейроморфных архитектур, аналоговых оптических вычислений, вычислений на фотонных резервуарах и интегрированных фотонных схемах, а также дается представление о будущих направлениях исследований и потенциальных применениях этих систем для обеспечения следующего поколения аппаратного обеспечения сверхвысокопроизводительных вычислений.

For citation

Machnev A.A., Melnikov I.V., Nadezhdin E.R., Sokolov V.A., Stupin D.D. Photonic technologies for implementing neuromorphic computing: results, research directions, and AI hardware // Journal of Radio Electronics. – 2026. – No. 2. https://doi.org/10.30898/1684-1719.2026.2.12

References

  1. Brückerhoff-Plückelmann F. et al. Broadband photonic tensor core with integrated ultra-low crosstalk wavelength multiplexers // Nanophotonics. - 2022. - Vol. 11. No. 17. - P. 1-10.
  2. Brückerhoff-Plückelmann F. et al. A large scale photonic matrix processor enabled by charge accumulation // Nanophotonics. - 20231. - Vol.2. - P. 812. https://doi.org/10.1515/nanoph-2022-0441
  3. Shi B., Calabretta N., Stabile R. Deep neural network through an InP SOA-based photonic integrated cross-connect // IEEE J. Sel. Top. Quantum Electron. - 2020. - Vol. 26, No. 1. - P. 1-11.
  4. Wu C. et al. Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network // Nat.Commun. - 2021. - Vol. 12, No. 96. - P. 1-8.
  5. Wetzstein G. et al. Inference in artificial intelligence with deep optics and photonics // Nature. - 2020. - Vol. 588. No. 7836. - P. 39-47.
  6. Feldmann J. et al. Parallel convolutional processing using an integrated photonic tensor core // Nature. - 2021. - Vol. 589. No. 7840. - P. 52-58.
  7. Xu X. et al. 11 TOPS photonic convolutional accelerator for optical neural networks // Nature. - 2021. - Vol. 589.No. 7840. - P. 44-51.
  8. Zhou T. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit // Nat. Photonics. - 2021. - Vol. 15. No. 5. - P. 367-373.
  9. Goodman J. W., Dias A. R, Woody L. M. Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms // Opt. Lett. - 1978. - Vol. 2. No. 1. - P. 1-3.
  10. Chang J. et al. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification // Sci. Rep. - 2018. - Vol. 8. No. 1. - P. 12324.
  11. Zuo Y. et al. All-optical neural network with nonlinear activation functions // Optica. - 2019. - Vol. 6. No. 9. - P. 1132-1137.
  12. Inagaki T. et al. Large-scale Ising spin network based on degenerate optical parametric oscillators // Nat Photon/ - 2016. 10(6): 415-9. https://doi.org/10.1038/nphoton.2016.68
  13. Larger L. et al. Photonic information processing beyond turing: An optoelectronic implementation of reservoir computing // Opt Express. - 2012. - 20(3):3241-9. https://doi.org/10.1364/OE.20.003241
  14. Takano K. et al.Compact reservoir computing with a photonic integrated circuit // Optics Express. - 2018. - 26(22): 29424-39. https://doi.org/10.1364/OE.26.029424
  15. M. Tan. Microwave and RF Photonic Fractional Hilbert Transformer Based on a 50 GHz Kerr Micro-Comb // Journal of Lightwave Technology. - 2019. - Vol. 37. No. 24. - P. 6097-6104. https://doi.org/10.1109/JLT.2019.2946606
  16. Tan M. et al. RF and microwave fractional differentiator based on photonics // IEEE Transactions on Circuits and Systems II: Express Briefs. Early Access. - 2020. - V. 67. - I. 11. - P. 2767 - 2771. https://doi.org/10.1109/TCSII.2020.2965158
  17. Kippenberg T. J., Holzwarth R., Diddams S. A. Microresonator-based optical frequency combs // Science. - 2011. -Vol. 332. - P. 555-559.
  18. Savchenkov A. A. et al. Tunable optical frequency comb with a crystalline whispering gallery mode resonator // Physics Review Letters. -2008. -Vol. 101. -P. 093902.
  19. Pasquazi A. et al. Micro-combs: a novel generation of optical sources // Physics Reports. - 2018. - Vol. 729. -P. 1-81.
  20. Ferrera M. et al. Low-power continuous-wave nonlinear optics in doped silica glass integrated waveguide structures // Nat. Photonics. -2008. -Vol. 2 (12). - P. 737-740.
  21. Peccianti M. et al. Demonstration of a stable ultrafast laser based on a nonlinear microcavity // Nat.Commun. - 2012. -Vol. 3 765. - P. 1-6.
  22. Pasquazi A.et al. Stable, dual mode, high repetition rate mode-locked laser based on a microring resonator // Opt. Exp. - 2012. - Vol. 20. No. 24. - P. 27355-27362.
  23. Pasquazi A. et al. Self-locked optical parametric oscillation in a CMOS compatible microring resonator: a route to robust optical frequency comb generation on a chip // Opt. Exp. -2013. - Vol. 21. No. 11. - P. 13333-13341.
  24. Shastri B. J. et al. Spike processing with a graphene excitable laser // Sci. Rep. - 2016. - Vol. 6. - P. 19126.
  25. Feldmann J. et al. All-optical spiking neurosynaptic networks with self-learning capabilities // Nature. - 2019. Vol. 569. - P. 208-214.
  26. Duport F. et al. All-optical reservoir computing // Opt. Express. - 2012. 20(20):22783-95. https://doi.org/10.1109/JSTQE.2018.2836985
  27. Lang R., Kobayashi K. External optical feedback effects on semiconductor injection laser properties // IEEE Journal of Quantum Electronis. - 1980. - Vol. 16. - No. 3. -P. 347-355. https://doi:10.1109/JQE.1980.1070479
  28. Alsing P. M., Kovanis V., Gavrielides A., Erneux T. Lang and Kobayashi phase equation // Physical Review A. - 1996. -Vol. 53 (6). 4429-4434. https://doi.org/10.1103/PhysRevA.53.4429
  29. Huang L.et al. Mid-infrared modulators integrating silicon and black phosphorus photonics // Ang. Mater. Today Adv. 2021. 12. 100170 https://doi.org/10.1016/j.mtadv.2021.100170
  30. Sorianello V. et al. Graphene-silicon phase modulators with gigahertz bandwidth // Nat. Photonics. - 2018. -Vol. 12. 40.
  31. Qiao Q. et al. Multifunctional mid-infrared photonic switch using a MEMS-based tunable waveguide coupler // Opt. Lett. - 2020. - Vol. 45. 5620. https://doi.org/10.1364/OL.400132
  32. Sagheer A. Kotb M. Time series forecasting of petroleum production using deep LSTM recurrent networks // Neurocomputing. - 2019. - V. 323. - P. 203-13. https://doi.org/10.1016/j.neucom.2018.09.082
  33. Chimmula VKR. Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks // Chaos. Solitons & Fractals. - 2020. - Vol. 135:109864. https://doi.org/10.1016/j.chaos.2020.109864
  34. Hochreiter S. Schmidhuber J. Long short-term memory // Neural Comput/ - 1997. - Vol. 9:1735-80. https://doi.org/10.1162/neco.1997.9.8.1735
  35. LeCun Y. Bengio Y. Hinton G. Deep learning // Nature. - 2015. -Vol. 521:436-44. https://doi.org/10.1038/nature14539
  36. Voulodimos A. Doulamis N. Doulamis A. Protopapadakis E. Deep learning for computer vision: A brief review // Comput intelligence Neurosci. - 2018
  37. Demirkiran C. Eris F. Wang G. Elmhurst J. Moore N. Harris NC. et al. An electro-photonic system for accelerating deep neural networks // - P. 01126. arXiv preprint (2021):arXiv:2109. https://doi.org/10.48550/arXiv.2109.01126
  38. Xu S., Ren Z., Dong B., J. Zhou,W. Liu, C. Lee. Mid-Infrared Silicon-on-Lithium-Niobate Electro-Optic Modulators Toward Integrated Spectroscopic Sensing Systems // Adv. Opt. Mater. - 2023. 11. - Р. 2202228.
  39. Li Z., Zhang H., B. Nguyen T. T., et al. // Photonics Res. - 2021, 9, B38.
  40. Zhou J., Zhang Z., Dong B., et al. // Opt. Express - 2010. 18. 1756
  41. Ma Y., Chang Y., Dong B., Wei J., W. Liu, C. Lee // ACS Nano. - 2021. - Vol. 15. 10084.
  42. Rios C., Du Q., Zhang Y., et al. // Sci. Rep. - 2016. 6. 22616.
  43. Shi B., N. Calabretta, Stabile R. Deep neural network through an InP SOA-based photonic integrated cross-connect // IEEE J. Sel. Top. Quantum Electron. - 2020. - Vol. 26. No. 1. - P. 1-11.
  44. Xu Q., Soref R. Reconfigurable optical directed-logic circuits using microresonator-based optical switches, Opt. Express, - 2011. - Vol. 19. No. 6. - P. 5244-5259.
  45. Yang L., Ji R., Ding J., Xu Q. On-chip CMOS-compatible optical signal processor // Opt. Express. - 2012. - Vol. 20, No. 12, - P. 13560-13565.
  46. Tait A. N., Nahmias M. A., Shastri B. J., Prucnal P. R. Broadcast and weight: an integrated network for scalable photonic spike processing // J. Lightwave Technol. - 2014. - Vol. 32. No. 21. - P. 4029-4041.
  47. Tait A. N., de Lima T. F., Zhou E., et al. Neuromorphic photonic networks using silicon photonic weight banks // Sci. Rep. - 2017. - Vol. 7. No. 1. - P. 7430.
  48. Tait A. N., de Lima T. F., Nahmias M. A., Shastri B. J., Prucnal P. R. Multi-channel control for microring weight banks // Opt. Express. - 2000. - Vol. 24., No. 8. - P. 8895-8906.
  49. Tait A. N., de Lima T. F., Nahmias M. A., B. J. Shastri, P. R. Prucnal. Continuous calibration of microring weights for analog optical networks // IEEE Photon. Technol. Lett. - 2016. - Vol. 28. - No. 8. - P. 887-890.
  50. Tait A. N., Xu A. X., de Lima T. F., et al. Microring weight banks // IEEE J. Sel. Top. Quantum Electron. - 2016. - Vol. 22. - No. 6. - P. 312-325.
  51. Huang C., Bilodeau S., de Lima T. F., et al. Demonstration of scalable microring weight bank control for large-scale photonic integrated circuits // APL Photonics - 2020. - Vol. 5. - No. 4. - P. 040803.
  52. Xu S., Wang J., Zou W. Optical convolutional neural network with WDM-based optical patching and microring weighting banks // IEEE Photon. Technol. Lett. - 2021. - Vol. 33. No. 2. - P. 89-92.
  53. Bangari V., Marquez B. A., Miller H., et al. Digital electronics and analog photonics for convolutional neural networks (DEAP-CNNs) // IEEE J. Sel. Top. Quantum Electron. - 2020. - Vol. 26. No. 1. - P. 1-13.
  54. Huang C., Fujisawa S., de Lima T. F., et al. A silicon photonic-electronic neural network for fibre nonlinearity compensation // Nat. Electron. - 2021. - Vol. 4. No. 11. - P. 837-844.
  55. Shi B., Calabretta N., Stabile R. Deep neural network through an InP SOA-based photonic integrated cross-connect // IEEE J. Sel. Top. Quantum Electron., - 2020. - Vol. 26. - No. 1. - P. 1-11.
  56. Mourgias-Alexandris G., Tsakyridis A., Passalis N., et al. An all-optical neuron with sigmoid activation function // Opt. Express. - 2019. - Vol. 27. No. 7. - P. 9620.
  57. Shi B., Calabretta N., Stabile R. InP photonic integrated multi-layer neural networks: architecture and performance analysis // APL Photonics. - 2022. - Vol. 7. No. 1. -P. 010801.
  58. Chakraborty I., Saha G. Sengupta, A., K. Roy. Toward fast neural computing using all-photonic phase change spiking neurons // Sci. Rep. - 2018. - Vol. 8. No. 1. - P. 12980.
  59. Li X., Youngblood N., Zhou W., et al. On-chip phase change optical matrix multiplication core // in IEEE Int. Electron Devices Meet. - 2020. - P. 7.5.1-7.5.4.
  60. Nahmias M. A., Peng H., de Lima T. F., et al. A teraMAC neuromorphic photonic processor // in IEEE hotonics Conf. (IPC). - 2018. - P. 1-2.
  61. Shainline J. M., Buckley S. M., McCaughan A. N., et al. Circuit designs for superconducting optoelectronic loop neurons // J. Appl. Phys. - 2018. - Vol. 124. No. 15. - P. 152130.
  62. Shastri J., Tait A. N., Prucnal P. R. A leaky integrate-and-fire laser neuron for ultrafast cognitive computing // IEEE J. Sel. Top. Quantum Electron. - 2013. - Vol. 19, No. 5, - P. 1-12.
  63. Tait A. N., Chang J., Shastri B. J. Nahmias, M. A., Prucnal P. R. Demonstration of WDM weighted addition for principal component analysis // Opt. Express - 2015. - Vol. 23, No. 10, - P. 12758-12765.
  64. Xu X., Moss D., Tan M., J. Wu, Morandotti R. Photonic perceptron based on a kerr microcomb for high-speed, scalable, optical neural networks // Laser Photon. Rev. - 2020. - Vol. 14. - No. 10. - P. 44-51.
  65. Mourgias-Alexandris G., Dabos G., Passalis N., Totović A., A. Tefas, and N. Pleros. All-optical WDM recurrent neural networks with gating // IEEE J. Sel. Top. Quantum Electron. - 2020. - Vol. 26, No. 5, - P. 1-7.
  66. Totovic A., Giamougiannis G., Tsakyridis A., et al. Programmable photonic neural networks combining WDM with coherent linear optics // Sci. Rep. - 2022. - Vol. 12, No. 1, p. 5605.
  67. Xiang J., Tao Z., Li X., et al. Metamaterial-enabled arbitrary on-chip spatial mode manipulation // Light Sci. Appl. - 2022. - Vol. 11, No. 168, - P. 1-11.
  68. Zhou Y., Braverman B., Fyffe A., et al. High-fidelity spatial mode transmission through a 1-km-long multimode fiber via vectorial time reversal // Nat.Commun., - 2021. - Vol. 12, No. 1866, - P. 1-7.
  69. Jiang W., Miao J., Li T.Compact silicon 10-mode multi/demultiplexer for hybrid mode- and polarization-division multiplexing system // Sci. Rep. - 2019. - Vol. 9, No. 13233, - P. 1-15.
  70. Feldmann J., Youngblood N., C. D. Wright, H. Bhaskaran, W. H. P. Pernice // Nature. - 2019. - Vol. 569. 208.
  71. Perez-Lopez D., Lopez A., P. DasMahapatra, J. Capmany // Nat.Commun. - 2020. -Vol. 11. 6359.
  72. Prabhu M., Roques-Carmes C., Shen Y., N. Harris, L. Jing, J. Carolan, R. Hamerly, T. Baehr-Jones, M. Hochberg, V. ˇCeperi'c, J. D. Joannopoulos, D. R. Englund, M. Soljacic // Optica. - 2020. 7. 551.
  73. Wu C., Yang X., H. Yu, R. Peng, Takeuchi I., Y. Chen, M. Li // Sci. Adv. - 2022. 8. 2956.
  74. Xu S., Wang J., S. Yi, W. Zou // Nat.Commun. - 2022. 13. 7970.
  75. Bai B., Yang Q., Shu H., L. Chang, F. Yang, B. Shen, Z. Tao, J. Wang, S. Xu, W. Xie, W. Zou, W. Hu, J. E. Bowers, X. Wang // Nat.Commun. - 2023. 14. 66.
  76. Cheng J., Zhang W., W. Gu, H. Zhou, J. Dong, X. Zhang // ACS Photonics. - 2022. 10. 2173.
  77. Xu X., Tan M., Corcoran B. et al. 11 TOPS photonic convolutional accelerator for optical neural networks // Nature - 2021. 589. 44.
  78. Shen Y., Harris N. C., Skirlo S. et al. Deep Learning with Coherent Nanophotonic Circuits // Nat. Photonics - 2017, 11, 441.
  79. Zhang H., Gu M., Jiang X. D. et al. An optical neural chip for implementing complex-valued neural network // Nat.Commun. - 2021. - Vol. 12. - P. 457.
  80. Ashtiani F., Geers A. J., Aflatouni F. An on-chip photonic deep neural network for image classification // Nature/ - 2022. - Vol. 606. -P 501. https://doi.org/10.1038/s41586-022-04714-0
  81. Tanomura R. Tang R, Umezaki T, Soma G, Tanemura T, Nakano Y. Scalable and robust photonic integrated unitary converter based on multiplane light conversion // Phys Rev Appl. - 2022. -Vol. 17(2):024071 https://doi.org/10.1103/PhysRevApplied.17.024071
  82. Qu Y. Zhu H, Shen Y, Zhang J, Tao C, Ghosh P, et al. Inverse design of an integrated-nanophotonics optical neural network // Sci Bull. - 2020. -Vol. 65(14):1177-83. https://doi.org/10.1016/j.scib.2020.03.042
  83. Zarei S. Marzban MR, Khavasi A.Integrated photonic neural network based on silicon metalines // Opt Express. - 2020. -Vol. 28(24):36668-84. https://doi.org/10.1364/oe.404386.
  84. Khoram E., Chen A., Liu D, Ying L., Wang Q., Yuan M. et al. Nanophotonic media for artificial neural inference // Photon Res. - 2019.- Vol. 7(8):823-7. https://doi.org/10.48550/arXiv.1810.07815
  85. Huang C. Sorger VJ,Miscuglio M, Al-Qadasi M, Mukherjee A, Lampe L, et al. Prospects and applications of photonic neural networks // Adv Phys X. - 2022. -Vol. 7: 1981155. https://doi.org/10.1080/23746149.2021.1981155
  86. Wu J. Lin X, Guo Y, Liu J, Fang L, Jiao S, et al. Analog optical computing for artificial intelligence // Engineering. - 2021. -Vol. 10:133-45. https://doi.org/10.1016/j.eng.2021.06.021
  87. Xu X. Tan M, Corcoran B, Wu J, Boes A, Nguyen TG, et al. 11 TOPS photonic convolutional accelerator for optical neural networks // Nature. - 2021.- Vol. 589:44-51. https://doi.org/10.1038/s41586-020-03063-0
  88. Li Y. Xue Y, Tian L. Deep speckle correlation: A deep learning approach toward scalable imaging through scattering media // Optica/ - 2018. -Vol. 5:1181-90. https://arxiv.org/abs/1806.04139v2
  89. Brossollet C. Cappelli A, Carron I, Chaintoutis C, Chatelain A, Daudet L, et al. LightOn optical processing unit: Scaling-up AI and HPC with a non von Neumann co-processor (2021) // arXiv preprint arXiv:2107.11814.
  90. Launay J. Poli I, Müller K, Carron I, Daudet L, Krzakala F, et al. Light-in-the loop: Using a photonics co-processor for scalable training of neural networks // arXiv preprint arXiv:2006.01475 (2020). https://doi.org/10.48550/arXiv.2006.01475
  91. Tanaka G., Yamane T. et al. Recent advances in physical reservoir computing: A review // Neural Networks. - 2019. 115: 100-23. https://doi.org/10.48550/arXiv.1808.04962
  92. Li Y., Yu H. et al. Graphene-Based Floating-Gate Nonvolatile Optical Switch // IEEE Photonics Technol. Lett. - 2016. - Vol. 28. - P. 284.
  93. Rios C. et al.Integrated all-photonic non-volatile multi-level memory // Nat. Photonics. - 2015. -Vol. 9, - P. 725.