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Unsupervised character recognition with graphene memristive synapses

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Abstract

Memristive devices being applied in neuromorphic computing are envisioned to significantly improve the power consumption and speed of future computing platforms. The materials used to fabricate such devices will play a significant role in their viability. Graphene is a promising material, with superb electrical properties and the ability to be produced in large volumes. In this paper, we demonstrate that a graphene-based memristive device could potentially be used as synapses within spiking neural networks (SNNs) to realise spike timing-dependant plasticity for unsupervised learning in an efficient manner. Specifically, we verify the operation of two SNN architectures tasked for single-digit (0–9) classification: (i) a single layer network, where inputs are presented in \(5\times 5\) pixel resolution, and (ii) a larger network capable of classifying the dataset. Our work presents the first investigation and large-scale simulation of the use of graphene memristive devices to perform a complex pattern classification task. In favour of reproducible research, we will make our code and data publicly available. This can pave the way for future research in using graphene devices with memristive capabilities in neuromorphic computing architectures.

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Availability of data and materials

The data and materials will be made publicly available.

Code availability

Our code is made available at: https://github.com/jc427648/SNN-GrapheneSynapses.git.

References

  1. Chua L (1971) Memristor—the missing circuit element. IEEE Trans Circuit Theory 18(5):507–519. https://doi.org/10.1109/TCT.1971.1083337

    Article  Google Scholar 

  2. Maranhão G, Guimarães JG (2021) Low-power hybrid memristor-CMOS spiking neuromorphic STDP learning system. IET Circuits Devices Syst 15(3):237–250. https://doi.org/10.1049/cds2.12018

    Article  Google Scholar 

  3. Rahimi Azghadi M, Chen YC, Eshraghian JK, Chen J, Lin CY, Amirsoleimani A et al (2020) Complementary metal-oxide semiconductor and memristive hardware for neuromorphic computing. Adv Intell Syst 2(5):1900189. https://doi.org/10.1002/aisy.201900189

    Article  Google Scholar 

  4. Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nature 453(7191):80–83. https://doi.org/10.1038/nature06932

    Article  Google Scholar 

  5. Sun K, Chen J, Yan X (2021) The future of memristors: materials engineering and neural networks. Adv Funct Mater 31(8):2006773. https://doi.org/10.1002/adfm.202006773

    Article  Google Scholar 

  6. Ambrogio S, Ciocchini N, Laudato M, Milo V, Pirovano A, Fantini P et al (2016) Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM) synapses. Front Neurosci 10:56. https://doi.org/10.3389/fnins.2016.00056

    Article  Google Scholar 

  7. Velev JP, Duan CG, Burton JD, Smogunov A, Niranjan MK, Tosatti E et al (2009) Magnetic tunnel junctions with ferroelectric barriers: prediction of four resistance states from first principles. Nano Lett 9(1):427–432. https://doi.org/10.1021/nl803318d

    Article  Google Scholar 

  8. Garcia V, Fusil S, Bouzehouane K, Enouz-Vedrenne S, Mathur ND, Barthélémy A et al (2009) Giant tunnel electroresistance for non-destructive readout of ferroelectric states. Nature 460(7251):81–84. https://doi.org/10.1038/nature08128

    Article  Google Scholar 

  9. Solanki A, Guerrero A, Zhang Q, Bisquert J, Sum TC (2020) Interfacial mechanism for efficient resistive switching in ruddlesden-popper perovskites for non-volatile memories. J Phys Chem Lett 11(2):463–470. https://doi.org/10.1021/acs.jpclett.9b03181

    Article  Google Scholar 

  10. Ahn M, Park Y, Lee SH, Chae S, Lee J, Heron JT et al (2021) Memristors based on (Zr, Hf, Nb, Ta, Mo, W) high-entropy oxides. Adv Electronic Mater 7(5):2001258. https://doi.org/10.1002/aelm.202001258

    Article  Google Scholar 

  11. Zhang C, Zhou H, Chen S, Zhang G, Yu ZG, Chi D et al (2021) Recent progress on 2D materials-based artificial synapses. Critical Reviews in Solid State and Materials Sciences. 1–26. https://doi.org/10.1080/10408436.2021.1935212

  12. Kwon KC, Baek JH, Hong K, Kim SY, Jang HW (2022) Memristive devices based on two-dimensional transition metal chalcogenides for neuromorphic computing. Nano Micro Lett 14(1):58. https://doi.org/10.1007/s40820-021-00784-3

    Article  Google Scholar 

  13. Zhou Z, Yang F, Wang S, Wang L, Wang X, Wang C et al (2021) Emerging of two-dimensional materials in novel memristor. Front Phys 17(2):23204. https://doi.org/10.1007/s11467-021-1114-5

    Article  Google Scholar 

  14. Jacob MV, Rawat RS, Ouyang B, Bazaka K, Kumar DS, Taguchi D et al (2015) Catalyst-free plasma enhanced growth of graphene from sustainable sources. Nano Lett 15(9):5702–5708. PMID: 26263025. https://doi.org/10.1021/acs.nanolett.5b01363

  15. Burr GW (2019) A role for analogue memory in AI hardware. Nat Mach Intell 1(1):10–11. https://doi.org/10.1038/s42256-018-0007-y

    Article  MathSciNet  Google Scholar 

  16. Wang J, Zhuge F (2019) Memristive synapses for brain-inspired computing. Adv Mater Technol 4(3):1800544. https://doi.org/10.1002/admt.201800544

    Article  Google Scholar 

  17. Xiao Z, Yan B, Zhang T, Huang R, Yang Y (2022) Memristive devices based hardware for unlabeled data processing. Neuromorphic Comput Eng 2(2):022003. https://doi.org/10.1088/2634-4386/ac734a

  18. Huang M, Li Z, Zhu H (2022) Recent advances of graphene and related materials in artificial intelligence. Adv Intell Syst 4(10):2200077

    Article  Google Scholar 

  19. Li R, Huang P, Feng Y, Zhou Z, Zhang Y, Ding X et al (2022) Hardware demonstration of srdp neuromorphic computing with online unsupervised learning based on memristor synapses. Micromachines 13(3):433

    Article  Google Scholar 

  20. Xiang Y, Huang P, Zhao Y, Zhao M, Gao B, Wu H et al (2019) Impacts of state instability and retention failure of filamentary analog rram on the performance of deep neural network. IEEE Trans Electron Dev 66(11):4517–4522. https://doi.org/10.1109/TED.2019.2931135

    Article  Google Scholar 

  21. Vaila R, Chiasson J, Saxena V (2020) A deep unsupervised feature learning spiking neural network with binarized classification layers for the emnist classification. IEEE Trans Emerg Top Comput Intell 6(1):124–135. https://doi.org/10.1109/TETCI.2020.3035164

    Article  Google Scholar 

  22. Chellappa R, Theodoridis S, van Schaik A (2021) Advances in machine learning and deep neural networks. Proc IEEE 109(5):607–611. https://doi.org/10.1109/JPROC.2021.3072172

    Article  Google Scholar 

  23. Zhang W, Li P (2019) Information-theoretic intrinsic plasticity for online unsupervised learning in spiking neural networks. Front Neurosci 13:31. https://doi.org/10.3389/fnins.2019.00031

    Article  Google Scholar 

  24. Zambrano D, Nusselder R, Scholte HS, Bohté SM (2019) Sparse computation in adaptive spiking neural networks. Front Neurosci 12:987. https://doi.org/10.3389/fnins.2018.00987

    Article  Google Scholar 

  25. Zenke F, Ganguli S (2017) SuperSpike: supervised learning in multilayer spiking neural networks. Neural Comput 05:30. https://doi.org/10.1162/neco_a_01086

    Article  Google Scholar 

  26. Neftci EO, Mostafa H, Zenke F (2019) Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process Mag 36(6):51–63. https://doi.org/10.1109/MSP.2019.2931595

    Article  Google Scholar 

  27. Eshraghian JK, Ward M, Neftci E, Wang X, Lenz G, Dwivedi G, et al. (2021) Training spiking neural networks using lessons from deep learning. arXiv preprint arXiv:2109.12894

  28. Azghadi MR, Linares-Barranco B, Abbott D, Leong PH (2017) A hybrid CMOS-memristor neuromorphic synapse. IEEE Trans Biomed Circuits Syst 11(2):434–445. https://doi.org/10.1109/TBCAS.2016.2618351

    Article  Google Scholar 

  29. Rahimi Azghadi M, Al-Sarawi S, Abbott D, Iannella N (2013) A neuromorphic VLSI design for spike timing and rate based synaptic plasticity. Neural Netw 45:70–82. Neuromorphic Engineering: From Neural Systems to Brain-Like Engineered Systems. https://doi.org/10.1016/j.neunet.2013.03.003

  30. Gq Bi, Mm Poo (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18(24):10464–10472. https://doi.org/10.1523/JNEUROSCI.18-24-10464.1998. www.jneurosci.org/content/18/24/10464.full.pdf

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

    Article  Google Scholar 

  32. Brivio S, Ly DRB, Vianello E, Spiga S (2021) Non-linear memristive synaptic dynamics for efficient unsupervised learning in spiking neural networks. Front Neurosci 15:27. https://doi.org/10.3389/fnins.2021.580909

    Article  Google Scholar 

  33. Querlioz D, Bichler O, Dollfus P, Gamrat C (2013) Immunity to device variations in a spiking neural network with memristive nanodevices. IEEE Trans Nanotechnol 05(12):288–295. https://doi.org/10.1109/TNANO.2013.2250995

    Article  Google Scholar 

  34. Kim S, Yoon J, Kim HD, Choi SJ (2015) Carbon nanotube synaptic transistor network for pattern recognition. ACS Appl Mater Interfaces 7(45):25479–25486. https://doi.org/10.1021/acsami.5b08541

    Article  Google Scholar 

  35. Hansen M, Zahari F, Ziegler M, Kohlstedt H (2017) Double-barrier memristive devices for unsupervised learning and pattern recognition. Front Neurosci 11:91. https://doi.org/10.3389/fnins.2017.00091

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Nandakumar SR, Rajendran B (2020) Bio-mimetic synaptic plasticity and learning in a sub-500mV Cu/SiO2/W memristor. Microelectronic Eng, 226. https://doi.org/10.1016/j.mee.2020.111290

  38. Boybat I, Le Gallo M, Nandakumar SR, Moraitis T, Parnell T, Tuma T et al (2018) Neuromorphic computing with multi-memristive synapses. Nat Commun 9(1):2514. https://doi.org/10.1038/s41467-018-04933-y

    Article  Google Scholar 

  39. Bill J, Legenstein R (2014) A compound memristive synapse model for statistical learning through STDP in spiking neural networks. Front Neurosci, 8. https://doi.org/10.3389/fnins.2014.00412

  40. Covi E, Brivio S, Serb A, Prodromakis T, Fanciulli M, Spiga S (2016) Analog memristive synapse in spiking networks implementing unsupervised learning. Front Neurosci 10:482. https://doi.org/10.3389/fnins.2016.00482

    Article  Google Scholar 

  41. Qu L, Zhao Z, Wang L, Wang Y (2020) Efficient and hardware-friendly methods to implement competitive learning for spiking neural networks. Neural Comput Appl 32(17):13479–13490. https://doi.org/10.1007/s00521-020-04755-4

    Article  Google Scholar 

  42. Demin VA, Nekhaev DV, Surazhevsky IA, Nikiruy KE, Emelyanov AV, Nikolaev SN et al (2021) Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network. Neural Netw 134:64–75. https://doi.org/10.1016/j.neunet.2020.11.005

    Article  Google Scholar 

  43. Hajiabadi Z, Shalchian M (2021) Memristor-based synaptic plasticity and unsupervised learning of spiking neural networks. J Comput Electronics 20(4):1625–1636. https://doi.org/10.1007/s10825-021-01719-2

    Article  Google Scholar 

  44. Kopelevich Y, Bud’ko S, Cooper DR, D’Anjou B, Ghattamaneni N, Harack B et al (2012) Experimental review of graphene. ISRN Condensed Matter Phys 2012:501686. https://doi.org/10.5402/2012/501686

    Article  Google Scholar 

  45. Akinwande D, Brennan CJ, Bunch JS, Egberts P, Felts JR, Gao H et al (2017) A review on mechanics and mechanical properties of 2D materials-Graphene and beyond. Extreme Mech Lett 13:42–77. https://doi.org/10.1016/j.eml.2017.01.008

    Article  Google Scholar 

  46. Chen Y, Zhou Y, Zhuge F, Tian B, Yan M, Li Y, et al (2019) Graphene-ferroelectric transistors as complementary synapses for supervised learning in spiking neural network. 2D Mater Appl 3(1):31. https://doi.org/10.1038/s41699-019-0114-6

  47. Schranghamer TF, Oberoi A, Das S (2020) Graphene memristive synapses for high precision neuromorphic computing. Nat Commun 11(1):5474. https://doi.org/10.1038/s41467-020-19203-z

    Article  Google Scholar 

  48. Sun Y, Lin Y, Zubair A, Xie D, Palacios T (2021) WSe2/graphene heterojunction synaptic phototransistor with both electrically and optically tunable plasticity. 2D Mater 8(3):035034. https://doi.org/10.1088/2053-1583/abfa6a

  49. Abunahla H, Halawani Y, Alazzam A, Mohammad B (2020) NeuroMem: analog graphene-based resistive memory for artificial neural networks. Sci Rep 10(1):9473. https://doi.org/10.1038/s41598-020-66413-y

    Article  Google Scholar 

  50. Qi M, Cao S, Yang L, You Q, Shi L, Wu Z (2020) Uniform multilevel switching of graphene oxide-based RRAM achieved by embedding with gold nanoparticles for image pattern recognition. Appl Phys Lett 116(16):163503. https://doi.org/10.1063/5.0003696

    Article  Google Scholar 

  51. Romero FJ, Toral A, Medina-Rull A, Moraila-Martinez CL, Morales DP, Ohata A et al (2020) Resistive switching in graphene oxide. Front Mater 7:17. https://doi.org/10.3389/fmats.2020.00017

    Article  Google Scholar 

  52. Porro S, Accornero E, Pirri CF, Ricciardi C (2015) Memristive devices based on graphene oxide. Carbon 85:383–396. https://doi.org/10.1016/j.carbon.2015.01.011

    Article  Google Scholar 

  53. Liu B, Liu Z, Chiu IS, Di M, Wu Y, Wang JC et al (2018) Programmable synaptic metaplasticity and below femtojoule spiking energy realized in graphene-based neuromorphic memristor. ACS Appl Mater Interfaces 10(24):20237–20243. https://doi.org/10.1021/acsami.8b04685

    Article  Google Scholar 

  54. Krishnaprasad A, Choudhary N, Das S, Dev D, Kalita H, Chung HS et al (2019) Electronic synapses with near-linear weight update using MoS2/graphene memristors. Appl Phys Lett 115(10):103104. https://doi.org/10.1063/1.5108899

    Article  Google Scholar 

  55. Feng X, Liu X, Ang KW (2020) 2D photonic memristor beyond graphene: progress and prospects. Nanophotonics. https://doi.org/10.1515/nanoph-2019-0543

  56. Cao G, Meng P, Chen J, Liu H, Bian R, Zhu C et al (2021) 2D material based synaptic devices for neuromorphic computing. Adv Funct Mater 31(4):2005443. https://doi.org/10.1002/adfm.202005443

    Article  Google Scholar 

  57. Yalagala B, Khandelwal S, J D, Badhulika S (2019) Wirelessly destructible MgO-PVP-Graphene composite based flexible transient memristor for security applications. Mater Sci Semiconductor Process 104:104673. https://doi.org/10.1016/j.mssp.2019.104673

  58. Bi G, Poo M (2001) Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu Rev Neurosci 24:139–66. https://doi.org/10.1146/annurev.neuro.24.1.139

    Article  Google Scholar 

  59. Sahu DP, Jetty P, Jammalamadaka SN (2021) Graphene oxide based synaptic memristor device for neuromorphic computing. Nanotechnology 32(15):155701. https://doi.org/10.1088/1361-6528/abd978

  60. Wang H, Laurenciu NC, Jiang Y, Cotofana SD (2020) Compact graphene-based spiking neural network with unsupervised learning capabilities. IEEE Open J Nanotechnol 1:135–144. https://doi.org/10.1109/OJNANO.2020.3041198

    Article  Google Scholar 

  61. Wang H, Laurenciu NC, Jiang Y, Cotofana S (2021) Graphene-based artificial synapses with tunable plasticity. J Emerg Technol Comput Syst 17(4). https://doi.org/10.1145/3447778

  62. Wang Z, Liu C, Deng Y, Huang Z, He S, Guo D (2020) Carbon-based spiking neural network implemented with single-electron transistor and memristor for visual perception. In: 2020 IEEE 14th international conference on anti-counterfeiting, security, and identification (ASID), pp 143–146

  63. Wang H, Cucu Laurenciu N, Cotofana S (2021) A reconfigurable graphene-based spiking neural network architecture. IEEE Open J Nanotechnol, p 1. https://doi.org/10.1109/OJNANO.2021.3094761

  64. Hajri B, Aziza H, Mansour MM, Chehab A (2019) RRAM device models: a comparative analysis with experimental validation. IEEE Access 7:168963–168980. https://doi.org/10.1109/ACCESS.2019.2954753

    Article  Google Scholar 

  65. Pershin Y, Di Ventra M (2012) SPICE model of memristive devices with threshold. Radioengineering 04:22

    Google Scholar 

  66. Kvatinsky S, Ramadan M, Friedman EG, Kolodny A (2015) VTEAM: a general model for voltage-controlled memristors. IEEE Trans Circuits Syst II Exp Briefs 62(8):786–790. https://doi.org/10.1109/tcsii.2015.2433536

    Article  Google Scholar 

  67. Messaris I, Serb A, Stathopoulos S, Khiat A, Nikolaidis S, Prodromakis T (2018) A data-driven verilog-A ReRAM model. IEEE Trans Comput Aided Design Integrated Circuits Syst 37(12):3151–3162. https://doi.org/10.1109/TCAD.2018.2791468

    Article  Google Scholar 

  68. Jacob MV, Taguchi D, Iwamoto M, Bazaka K, Rawat RS (2017) Resistive switching in graphene-organic device: charge transport properties of graphene-organic device through electric field induced optical second harmonic generation and charge modulation spectroscopy [Journal Article]. Carbon 112:111–116. https://doi.org/10.1016/j.carbon.2016.11.005

    Article  Google Scholar 

  69. Rahimi Azghadi M, Moradi S, Fasnacht D, Ozdas M, Indiveri G (2015) Programmable spike-timing-dependent plasticity learning circuits in neuromorphic VLSI architectures. ACM J Emerg Technol Comput Syst 12(2). https://doi.org/10.1145/2658998

  70. Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14(6):1569–1572. https://doi.org/10.1109/TNN.2003.820440

    Article  MathSciNet  Google Scholar 

  71. Hodgkin AL, Huxley AF (1990) A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull Math Biol 52(1):25–71. https://doi.org/10.1007/BF02459568

    Article  Google Scholar 

  72. Lapique L (1907) Recherches quantitatives sur l’excitation electrique des nerfs traitee comme une polarization. J Physiol Pathol 9:620–635

    Google Scholar 

  73. Zamarreno-Ramos C, Camunas-Mesa LA, Perez-Carrasco JA, Masquelier T, Serrano-Gotarredona T, Linares-Barranco B (2011) On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. Front Neurosci 5:26. https://doi.org/10.3389/fnins.2011.00026

    Article  Google Scholar 

  74. Lammie C, Hamilton T, Azghadi MR (2018 ) Unsupervised character recognition with a simplified FPGA neuromorphic system. IEEE Explore, pp 1–5. https://doi.org/10.1109/ISCAS.2018.8351532

  75. Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3(9):919–926. https://doi.org/10.1038/78829

    Article  Google Scholar 

  76. Shi X, Zeng Z, Yang L, Huang Y (2018) Memristor-based circuit design for neuron with homeostatic plasticity. IEEE Trans Emer Top Comput Intell 2(5):359–370. https://doi.org/10.1109/TETCI.2018.2829914

    Article  Google Scholar 

  77. Lazar A, Pipa G, Triesch J (2007) Fading memory and time series prediction in recurrent networks with different forms of plasticity. Neural Netw 20(3):312–322. Echo State Networks and Liquid State Machines. https://doi.org/10.1016/j.neunet.2007.04.020

  78. Mihalas S, Niebur E (2009) A generalized linear integrate-and-fire neural model produces diverse spiking behaviors. Neural Comput 21(3):704–718. PMID: 18928368. https://doi.org/10.1162/neco.2008.12-07-680

  79. Akiba T, Sano S, Yanase T, Ohta T, Koyama M (2019) Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery; data mining. KDD ’19. New York, NY, USA: Association for Computing Machinery, pp 2623–2631. Available from: https://doi.org/10.1145/3292500.3330701

  80. Naous R, Al-Shedivat M, Salama KN (2016) Stochasticity modeling in memristors. IEEE Trans Nanotechnol 15(1):15–28. https://doi.org/10.1109/TNANO.2015.2493960

    Article  Google Scholar 

  81. Payvand M, Nair MV, Müller LK, Indiveri G (2019) A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: from mitigation to exploitation. Faraday Discussions 213:487–510

    Article  Google Scholar 

  82. Rieck JL, Hensling FV, Dittmann R (2021) Trade-off between variability and retention of memristive epitaxial SrTiO3 devices. APL Mater 9(2):021110

    Article  Google Scholar 

  83. Ford AJ, Jha R (2020) Memristive device variability performance impact on neuromorphic machine learning hardware. In: (2020) 11th International green and sustainable computing workshops (IGSC). IEEE, pp 1–7

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Acknowledgements

Ben Walters acknowledges the Domestic Research Training Program Scholarship (DRTPS) (Australia). Corey Lammie acknowledges the Domestic Prestige Research Training Program Scholarship (DPRTPS) (Australia), IBM PhD Fellowship (IBM, US), and the Circuits and Systems (CAS) Society Predoctoral Grant (IEEE, US).

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  1. College of Science and Engineering, James Cook University, 1 James Cook Drive, Townsville, QLD, 4811, Australia

    Ben Walters, Corey Lammie, Mohan V Jacob & Mostafa Rahimi Azghadi

  2. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Shuangming Yang

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Walters, B., Lammie, C., Yang, S. et al. Unsupervised character recognition with graphene memristive synapses. Neural Comput & Applic 36, 1569–1584 (2024). https://doi.org/10.1007/s00521-023-09135-2

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