Skip to main content
SpringerLink
Log in

Simulation Modeling of an Analog Impulse Neural Network Based on a Memristor Crossbar Using Parallel Computing Technologies

  • Published:
Russian Microelectronics Aims and scope Submit manuscript

Abstract

The issues of simulation modeling of an analog impulse neural network based on memristive elements in the problem of pattern recognition are studied. Simulation modeling allows us to configure the network at the level of a mathematical model, and subsequently use the obtained parameters directly in the process of operation. The network model is given as a dynamic system, which can consist of tens or hundreds of thousands of ordinary differential equations. Naturally, there is a need for an efficient and parallel implementation of an appropriate simulation model. Open multiprocessing (OpenMP) is used as the technology for parallelizing calculations, since it allows us to easily create multithreaded applications in various programming languages. The efficiency of parallelization is evaluated on the problem of modeling the process of training the network to recognize a set of five images of a size of 128 by 128 pixels, which leads to the solution of about 80 000 differential equations. In this problem, the calculations are accelerated by a factor of over six. According to the experimental data, the operating character of memristors is stochastic, as shown by the scatter in the current-voltage characteristics (VACs) when switching between high-resistance and low-resistance states. To take this feature into account, a memristor model with interval parameters is used, which gives upper and lower limits on the values of interest, and encloses the experimental curves in corridors. When simulating the operation of the entire analog self-learning impulse neural network, in each epoch of training, the parameters of the memristors are set randomly from the selected intervals. This approach makes it possible to dispense with the use of a stochastic mathematical apparatus, thereby further reducing computational costs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.

REFERENCES

  1. Merolla, P.A., Arthur, J.V., Alvarez-Icaza, R., Cassidy, A.S., Sawada, J., Akopyan, F., Jackson, B.L., Imam, N., Guo, Ch., Nakamura, Y., Brezzo, B., Vo, I., Esser, S.K., Appuswamy, R., Taba, B., Amir, A., Flickner, M.D., Risk, W.P., Manohar, R., and Modha, Dh.S., Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface, Science, 2014, vol. 345, no. 6197, pp. 668–673. https://doi.org/10.1126/science.1254642

    Article  CAS  ADS  PubMed  Google Scholar 

  2. Wong, H.-S.P., Lee, H.-Y., Yu, Sh., Chen, Y.-Sh., Wu, Y., Chen, P.-Sh., Lee, B., Chen, F.T., and Tsai, M.-J., Metal-oxide RRAM, Proc. IEEE, 2012, vol. 100, no. 6, pp. 1951–1970. https://doi.org/10.1109/JPROC.2012.2190369

    Article  CAS  Google Scholar 

  3. Yang, J.J., Strukov, D.B., and Stewart, D.R., Memristive devices for computing, Nat. Nanotechnol., 2013, vol. 8, no. 1, pp. 13–24. https://doi.org/10.1038/nnano.2012.240

    Article  CAS  ADS  PubMed  Google Scholar 

  4. Li, C., Hu, M., Li, Y., Ge, N., Montgomery, E., Zhang, J., Song, W., Dávila, N., Graves, C.E., Li, Zh., Strachan, J.P., Lin, P., Wang, Zh., Barnell, M., Wu, Q., Williams, R.S., Yang, J.J., and Xia, Q., Analogue signal and image processing with large memristor crossbars, Nat. Electron., 2018, vol. 1, pp. 52–59. https://doi.org/10.1038/s41928-017-0002-z

    Article  Google Scholar 

  5. Morozov, A.Yu., Abgaryan, K.K., and Reviznikov, D.L., Mathematical model of a neuromorphic network based on memristive elements, Chaos, Solitons Fractals, 2021, vol. 143, p. 110548. https://doi.org/10.1016/j.chaos.2020.110548

    Article  MathSciNet  Google Scholar 

  6. Morozov, A.Yu., Abgaryan, K.K., and Reviznikov, D.L., Mathematical modeling of a self-learning neuromorphic network based on nanosized memristive elements with 1T1R crossbar architecture, Russ. Microelectron., 2020, vol. 50, no. 8, pp. 628–637. https://doi.org/10.1134/S1063739721080060

    Article  Google Scholar 

  7. Diehl, P. and Cook, M., Unsupervised learning of digit recognition using spike-timing-dependent plasticity, Front. Comput. Neurosci., 2015, vol. 9, p. 99. https://doi.org/10.3389/fncom.2015.00099

    Article  PubMed Central  PubMed  Google Scholar 

  8. Ambrogio, S., Balatti, S., Milo, V., Carboni, R., Wang, Zh., Calderoni, A., Ramaswamy, N., and Ielmini, D., Neuromorphic learning and recognition with one-transistor-one-resistor synapses and bistable metal oxide RRAM, IEEE Trans. Electron Devices, 2016, vol. 63, no. 4, pp. 1508–1515. https://doi.org/10.1109/TED.2016.2526647

    Article  CAS  ADS  Google Scholar 

  9. Guo, Y., Wu, H., Gao, B., and Qian, H., Unsupervised learning on resistive memory array based spiking neural networks, Front. Neurosci., 2019, vol. 13, p. 812. https://doi.org/10.3389/fnins.2019.00812

    Article  PubMed Central  PubMed  Google Scholar 

  10. OpenMP. https://www.openmp.org/. Cited April 2, 2021.

  11. PVS-Studio is a static analyzer on guard of code quality, security (SAST), and code safety. https://pvs-studio.com/ru/a/0057/. Cited April 2, 2021.

  12. Rodriguez-Fernandez, A., Cagli, C., Perniola, L., Miranda, E., and Suñé, J., Characterization of HfO2-based devices with indication of second order memristor effects, Microelectron. Eng., 2018, vol. 195, pp. 101–106. https://doi.org/10.1016/j.mee.2018.04.006

    Article  CAS  Google Scholar 

  13. Teplov, G.S. and Gornev, E.S., Multilevel bipolar memristor model considering deviations of switching parameters in the Verilog-A language, Russ. Microelectron., 2019, vol. 48, no. 3, pp. 131–142. https://doi.org/10.1134/S0544126919030104

    Article  Google Scholar 

  14. Vasil’ev, V.A. and Chernov, P.S., Mathematical modeling of memristor in the presence of noise, Matematicheskoe Model., 2014, vol. 26, no. 1, pp. 122–132.

    Google Scholar 

  15. Morozov, A.Yu., Abgaryan, K.K., and Reviznikov, D.L., Simulation of the neuromorphic network operation taking into account stochastic effects, CEUR Workshop Proc., 2021, vol. 2930, pp. 84–91.

    Google Scholar 

  16. Morozov, A.Yu., Abgaryan, K.K., and Reviznikov, D.L., Mathematical modeling of an analogue self-learning neural network based on memristive elements taking into account stochastic switching dynamics, Nanobiotechnol. Rep., 2021, vol. 16, no. 6, pp. 767–776. https://doi.org/10.1134/S1992722321060157

    Article  Google Scholar 

  17. Morozov, A.Yu., Abgaryan, K.K., and Reviznikov, D.L., Interval model of a memristor crossbar network, Phys. Status Solidi (B), 2022, vol. 259, no. 11, p. 2200150. https://doi.org/10.1002/pssb.202200150

    Article  CAS  ADS  Google Scholar 

  18. Morozov, A.Yu. and Reviznikov, D.L., Interval approach to solving problems of parametric identification for dynamical systems, Differ. Equations, 2022, vol. 58, no. 7, pp. 952–965. https://doi.org/10.1134/S0012266122070084

    Article  MathSciNet  Google Scholar 

  19. Mladenov, V., Analysis of memory matrices with HfO2 memristors in a PSpice environment, Electronics, 2019, vol. 8, no. 4, p. 383. https://doi.org/10.3390/electronics8040383

    Article  CAS  Google Scholar 

  20. Zheng, G., Mohanty, S.P., Kougianos, E., and Okobiah, O., Polynomial metamodel integrated Verilog-AMS for memristor-based mixed-signal system design, IEEE 56th Int. Midwest Symp. on Circuits and Systems (MWSCAS), Columbus, Ohio, 2013, IEEE, 2013, pp. 916–919. https://doi.org/10.1109/MWSCAS.2013.6674799

  21. Martyshov, M.N., Emelyanov, A.V., Demin, V.A., Nikiruy, K.E., Minnekhanov, A.A., Nikolaev, S.N., Taldenkov, A.N., Ovcharov, A.V., Presnyakov, M.Yu., Sitnikov, A.V., Vasiliev, A.L., Forsh, P.A., Granovsky, A.B., Kashkarov, P.K., Kovalchuk, M.V., and Rylkov, V.V., Multifilamentary character of anticorrelated capacitive and resistive switching in memristive structures based on (Co-Fe-B)x(LiNbO3)100–x nanocomposite, Phys. Rev. Appl., 2020, vol. 14, no. 3, p. 34016. https://doi.org/10.1103/PhysRevApplied.14.034016

    Article  CAS  ADS  Google Scholar 

  22. Rylkov, V.V., Nikolaev, S.N., Demin, V.A., Emelyanov, A.V., Sitnikov, A.V., Nikiruy, K.E., Levanov, V.V., Presnyakov, M.Yu., Taldenkov, A.N., Vasiliev, A.L., Chernoglazov, K.Yu., Vedeneev, A.S., Kalinin, Yu.E., Granovsky, A.B., Tugushev, V.V., and Bugaev, A.S., Transport, magnetic, and memristive properties of a nanogranular (CoFeB)x(LiNbOy)100–x composite material, J. Exp. Theor. Phys., 2018, vol. 126, no. 3, pp. 353–367. https://doi.org/10.1134/S1063776118020152

    Article  CAS  ADS  Google Scholar 

  23. Photo hosting Pinterest. https://ru.pinterest.com/ pin/351912463120005/. Cited September 2, 2022.

Download references

Funding

This study was supported by the Russian Foundation for Basic Research, grant no. 19-29-03051 mk.

Author information

Authors and Affiliations

  1. Federal Research Center “Computer Science and Control,” Russian Academy of Sciences, 119333, Moscow, Russia

    A. Yu. Morozov, K. K. Abgaryan & D. L. Reviznikov

Authors
  1. A. Yu. Morozov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. K. Abgaryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. L. Reviznikov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. K. Abgaryan.

Ethics declarations

The authors declare that they have no conflicts of interest.

Additional information

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The article was prepared based on the materials of the report presented at the VI International Conference “Mathematical Modeling in Materials Science of Electronic Components,” Moscow, October 24–26, 2022.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Morozov, A.Y., Abgaryan, K.K. & Reviznikov, D.L. Simulation Modeling of an Analog Impulse Neural Network Based on a Memristor Crossbar Using Parallel Computing Technologies. Russ Microelectron 52, 786–792 (2023). https://doi.org/10.1134/S1063739723080024

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1063739723080024

Keywords:

Search

Navigation