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Investigation and mitigation of Mott neuronal oscillation fluctuation in spiking neural network

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Abstract

Mott devices, featuring low hardware cost and high energy efficiency, have been demonstrated as a key oscillatory element in artificial neurons to enable spiking neural networks (SNNs) such as conversion-based SNNs (CSNNs). However, there will be inevitably non-ideal fluctuation in the oscillation behavior, causing the accuracy degradation of networks. In this paper, we investigate the Mott neuronal oscillation fluctuation (NOF) through experiments and modeling. The results show that the NOF phenomenon conforms to Gaussian distribution and originates from thermal fluctuation induced switching voltage variations. We construct a two-layer CSNN for image recognition tasks to study the NOF effect and propose the activation function boundary (AFB) method to strengthen the stability of the network. The results indicate that AFB can improve the accuracy of CSNN by up to 15.5% by tightening output distribution.

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References

  1. Zhang Z H, Wang Z W, Shi T, et al. Memory materials and devices: from concept to application. InfoMat, 2020, 2: 261–290

    Article  Google Scholar 

  2. Wu L D, Wang Z W, Wang B W, et al. Emulation of biphasic plasticity in retinal electrical synapses for light-adaptive pattern pre-processing. Nanoscale, 2021, 13: 3483–3492

    Article  Google Scholar 

  3. Zhao Y L, Yang J L, Li B, et al. NAND-SPIN-based processing-in-MRAM architecture for convolutional neural network acceleration. Sci China Inf Sci, 2023, 66: 142401

    Article  Google Scholar 

  4. Han Y N, Xiang S Y, Zhang T R, et al. Conversion of a single-layer ANN to photonic SNN for pattern recognition. Sci China Inf Sci, 2024, 67: 112403

    Article  MathSciNet  Google Scholar 

  5. Pei J, Deng L, Ma C, et al. Multi-grained system integration for hybrid-paradigm brain-inspired computing. Sci China Inf Sci, 2023, 66: 142403

    Article  Google Scholar 

  6. Roy K, Jaiswal A, Panda P. Towards spike-based machine intelligence with neuromorphic computing. Nature, 2019, 575: 607–617

    Article  Google Scholar 

  7. Sengupta A, Ye Y T, Wang R, et al. Going deeper in spiking neural networks: VGG and residual architectures. Front Neurosci, 2019, 13: 95

    Article  Google Scholar 

  8. Zhang X M, Wang Z R, Song W H, et al. Experimental demonstration of conversion-based SNNs with 1T1R Mott neurons for neuromorphic inference. In: Proceedings of IEEE International Electron Devices Meeting, San Francisco, 2019

  9. Wang Z R, Rao M Y, Han J W, et al. Capacitive neural network with neuro-transistors. Nat Commun, 2018, 9: 3208

    Article  Google Scholar 

  10. Bao L, Kang J, Fang Y C, et al. Artificial shape perception retina network based on tunable memristive neurons. Sci Rep, 2018, 8: 13727

    Article  Google Scholar 

  11. Wu L D, Wang Z W, Bao L, et al. Implementation of neuronal intrinsic plasticity by oscillatory device in spiking neural network. IEEE Trans Electron Dev, 2022, 69: 1830–1834

    Article  Google Scholar 

  12. Fu Y Y, Zhou Y, Huang X, et al. Forming-free and annealing-free V/VOx/HfWOx/Pt device exhibiting reconfigurable threshold and resistive switching with high speed (<30 ns) and high endurance (> 1012/> 1010). In: Proceedings of IEEE International Electron Devices Meeting, San Francisco, 2021

  13. Yan B N, Yang Y C, Huang R. Memristive dynamics enabled neuromorphic computing systems. Sci China Inf Sci, 2023, 66: 200401

    Article  Google Scholar 

  14. Lee D, Kwak M, Moon K, et al. Various threshold switching devices for integrate and fire neuron applications. Adv Elect Mater, 2019, 5: 1800866

    Article  Google Scholar 

  15. Zhang X M, Zhuo Y, Luo Q, et al. An artificial spiking afferent nerve based on Mott memristors for neurorobotics. Nat Commun, 2020, 11: 51

    Article  Google Scholar 

  16. Woo J Y, Wang P N, Yu S M. Integrated crossbar array with resistive synapses and oscillation neurons. IEEE Electron Dev Lett, 2019, 40: 1313–1316

    Article  Google Scholar 

  17. Duan Q X, Jing Z K, Zou X L, et al. Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks. Nat Commun, 2020, 11: 3399

    Article  Google Scholar 

  18. Zhang X M, Wu Z, Lu J K, et al. Fully memristive SNNs with temporal coding for fast and low-power edge computing. In: Proceedings of IEEE International Electron Devices Meeting, San Francisco, 2021

  19. Kim G M, In J H, Kim Y S, et al. Self-clocking fast and variation tolerant true random number generator based on a stochastic Mott memristor. Nat Commun, 2021, 12: 2906

    Article  Google Scholar 

  20. Wang Z W, Zheng Q L, Kang J, et al. Self-activation neural network based on self-selective memory device with rectified multilevel states. IEEE Trans Electron Dev, 2020, 67: 4166–4171

    Article  Google Scholar 

  21. Bao L, Wang Z W, Wang B W, et al. Tunable stochastic oscillator based on hybrid VO2/TaOx device for compressed sensing. IEEE Electron Dev Lett, 2020, 42: 102–105

    Article  Google Scholar 

  22. Pickett M D, Stanley Williams R. Sub-100 fJ and sub-nanosecond thermally driven threshold switching in niobium oxide crosspoint nanodevices. Nanotechnology, 2012, 23: 215202

    Article  Google Scholar 

  23. Kumar S, Strachan J P, Williams R S. Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing. Nature, 2017, 548: 318–321

    Article  Google Scholar 

  24. Kumar S, Wang Z W, Davila N, et al. Physical origins of current and temperature controlled negative differential resistances in NbO2. Nat Commun, 2017, 8: 658

    Article  Google Scholar 

  25. Yuan R, Duan Q, Tiw P J, et al. A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system. Nat Commun, 2022, 13: 3973

    Article  Google Scholar 

  26. Shao Z W, Cao X, Luo H J, et al. Recent progress in the phase-transition mechanism and modulation of vanadium dioxide materials. NPG Asia Mater, 2018, 10: 581–605

    Article  Google Scholar 

  27. Schofield P, Bradicich A, Gurrola R M, et al. Harnessing the metal-insulator transition of VO2 in neuromorphic computing. Adv Mater, 2023, 35: 2205294

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2019YFB2205401), National Natural Science Foundation of China (Grant Nos. 61834001, 62025401, 61927901), Beijing Nova Program (Grant No. 20220484113), and 111 project (Grant No. B10081).

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Correspondence to Zongwei Wang or Yimao Cai.

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Wu, L., Wang, Z., Bao, L. et al. Investigation and mitigation of Mott neuronal oscillation fluctuation in spiking neural network. Sci. China Inf. Sci. 67, 122404 (2024). https://doi.org/10.1007/s11432-023-3745-y

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  • DOI: https://doi.org/10.1007/s11432-023-3745-y

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