Skip to main content
Log in

Neuromorphic computing with memristive devices

  • Review
  • Special Focus on Memristive Devices and Neuromorphic Computing
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

Technology advances in the last a few decades have resulted in profound changes in our society, from workplaces to living rooms to how we socialize with each other. These changes in turn drive further technology developments, as the exponential growth of data demands ever increasing computing power. However, improvements in computing capacity from device scaling alone is no longer sufficient, and new materials, devices, and architectures likely need to be developed collaboratively to meet present and future computing needs. Specifically, devices that offer co-located memory and computing characteristics, as represented by memristor devices and memristor-based computing systems, have attracted broad interest in the last decade. Besides tremendous appeal in data storage applications, memristors offer the potential for efficient hardware realization of neuromorphic computing architectures that can effectively address the memory and energy walls faced by conventional von Neumann computing architectures. In this review, we evaluate the state-of-the-art in memristor devices and systems, and highlight the potential and challenges of applying such devices and architectures in neuromorphic computing applications. New directions that can lead to general, efficient in-memory computing systems will also be discussed.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Yang J J, Strukov D B, Stewart D R. Memristive devices for computing. Nat Nanotech, 2013, 8: 13–24

    Article  Google Scholar 

  2. Kim K H, Gaba S, Wheeler D, et al. A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. Nano Lett, 2012, 12: 389–395

    Article  Google Scholar 

  3. Pershin Y V, Di Ventra M. Neuromorphic, digital, and quantum computation with memory circuit elements. Proc IEEE, 2012, 100: 2071–2080

    Article  Google Scholar 

  4. Gaba S, Knag P, Zhang Z Y, et al. Memristive devices for stochastic computing. In: Proceedings of IEEE International Symposium on Circuits and Systems, Melbourne, 2014. 2592–2595

    Google Scholar 

  5. Zidan M, Jeong Y J, Shin J H, et al. Field-programmable crossbar array (FPCA) for reconfigurable computing. IEEE Trans Multi-Scale Comput Syst, 2017. doi: 10.1109/TMSCS.2017.2721160

    Google Scholar 

  6. Borghetti J, Snider G S, Kuekes P J, et al. ‘Memristive’ switches enable ‘stateful’ logic operations via material implication. Nature, 2010, 464: 873–876

    Article  Google Scholar 

  7. Mead C. Neuromorphic electronic systems. Proc IEEE, 1990, 78: 1629–1636

    Article  Google Scholar 

  8. Indiveri G, Horiuchi T K. Frontiers in neuromorphic engineering. Front Neurosci, 2011, 5: 118

    Google Scholar 

  9. Chicca E, Stefanini F, Bartolozzi C, et al. Neuromorphic electronic circuits for building autonomous cognitive systems. Proc IEEE, 2014, 102: 1367–1388

    Article  Google Scholar 

  10. Gaba S, Sheridan P, Zhou J, et al. Stochastic memristive devices for computing and neuromorphic applications. Nanoscale, 2013, 5: 5872–5878

    Article  Google Scholar 

  11. Prezioso M, Merrikh-Bayat F, Hoskins B D, et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, 2015, 521: 61–64

    Article  Google Scholar 

  12. Indiveri G, Linares-Barranco B, Legenstein R, et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology, 2013, 24: 384010

    Article  Google Scholar 

  13. Zidan M A, Chen A, Indiveri G, et al. Memristive computing devices and applications. J Electroceram, 2017, 39: 4–20

    Article  Google Scholar 

  14. Chua L O, Kang S M. Memristive devices and systems. Proc IEEE, 1976, 64: 209–223

    Article  MathSciNet  Google Scholar 

  15. Strukov D B, Snider G S, Stewart D R, et al. The missing memristor found. Nature, 2008, 453: 80–83

    Article  Google Scholar 

  16. Govoreanu B, Kar G S, Chen Y Y, et al. 10×10 nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation. In: Proceedings of IEEE International Electron Devices Meeting, Washington, 2011

    Google Scholar 

  17. Torrezan A C, Strachan J P, Medeiros-Ribeiro G, et al. Sub-nanosecond switching of a tantalum oxide memristor. Nanotechnology, 2011, 22: 485203

    Article  Google Scholar 

  18. Lee M J, Lee C B, Lee D, et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5−x/TaO2−x bilayer structures. Nat Mater, 2011, 10: 625–630

    Article  Google Scholar 

  19. Valov I, Lu W D. Nanoscale electrochemistry using dielectric thin films as solid electrolytes. Nanoscale, 2016, 8: 13828–13837

    Article  Google Scholar 

  20. Younis A, Chu D, Lin X, et al. High-performance nanocomposite based memristor with controlled quantum dots as charge traps. ACS Appl Mater Interface, 2013, 5: 2249–2254

    Article  Google Scholar 

  21. Stoliar P, Rozenberg M, Janod E, et al. Nonthermal and purely electronic resistive switching in a Mott memory. Phys Rev B, 2014, 90: 45146

    Article  Google Scholar 

  22. Wong H S P, Raoux S, Kim S B, et al. Phase change memory. Proc IEEE, 2010, 98: 2201–2227

    Article  Google Scholar 

  23. Diao Z T, Li Z J, Wang S Y, et al. Spin-transfer torque switching in magnetic tunnel junctions and spin-transfer torque random access memory. J Phys-Condens Matter, 2007, 19: 165209

    Article  Google Scholar 

  24. Sheridan P M, Cai F X, Du C, et al. Sparse coding with memristor networks. Nat Nanotech, 2017, 12: 784–789

    Article  Google Scholar 

  25. Chang T, Jo S H, Kim K H, et al. Synaptic behaviors and modeling of a metal oxide memristive device. Appl Phys A, 2011, 102: 857–863

    Article  Google Scholar 

  26. Hasegawa T, Ohno T, Terabe K, et al. Learning abilities achieved by a single solid-state atomic switch. Adv Mater, 2010, 22: 1831–1834

    Article  Google Scholar 

  27. Jo S H, Chang T, Ebong I, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett, 2010, 10: 1297–1301

    Article  Google Scholar 

  28. Kim S, Choi S H, Lu W. Comprehensive physical model of dynamic resistive switching in an oxide memristor. ACS Nano, 2014, 8: 2369–2376

    Article  Google Scholar 

  29. Seo K, Kim I, Jung S, et al. Analog memory and spike-timing-dependent plasticity characteristics of a nanoscale titanium oxide bilayer resistive switching device. Nanotechnology, 2011, 22: 254023

    Article  Google Scholar 

  30. Kim S, Du C, Sheridan P, et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett, 2015, 15: 2203–2211

    Article  Google Scholar 

  31. Du C, Ma W, Chang T, et al. Biorealistic implementation of synaptic functions with oxide memristors through internal ionic dynamics. Adv Funct Mater, 2015, 25: 4290–4299

    Article  Google Scholar 

  32. Kuzum D, Yu S, Wong H S. Synaptic electronics: materials, devices and applications. Nanotechnology, 2013, 24: 382001

    Article  Google Scholar 

  33. Wang Z R, Joshi S, Savelev S E, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16: 101–108

    Article  Google Scholar 

  34. Zidan M A, Jeong Y J, Lu W D. Temporal learning using second-order memristors. IEEE Trans Nanotechnol, 2017, 16: 721–723

    Article  Google Scholar 

  35. Ma W, Chen L, Du C, et al. Temporal information encoding in dynamic memristive devices. Appl Phys Lett, 2015, 107: 193101

    Article  Google Scholar 

  36. Zhu X, Du C, Jeong Y J, et al. Emulation of synaptic metaplasticity in memristors. Nanoscale, 2017, 9: 45–51

    Article  Google Scholar 

  37. Yang Y, Chen B, Lu W D. Memristive physically evolving networks enabling the emulation of heterosynaptic plasticity. Adv Mater, 2015, 27: 7720–7727

    Article  Google Scholar 

  38. Merolla P A, Arthur J V, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345: 668–673

    Article  Google Scholar 

  39. Benjamin B V, Gao P, McQuinn E, et al. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc IEEE, 2014, 102: 699–716

    Article  Google Scholar 

  40. Furber S B, Galluppi F, Temple S, et al. The SpiNNaker project. Proc IEEE, 2014, 102: 652–665

    Article  Google Scholar 

  41. Schemmel J, Briiderle D, Griibl A, et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling. In: Proceedings of IEEE International Symposium on Circuits and Systems, Paris, 2010. 1947–1950

    Google Scholar 

  42. Pfeil T, Grübl A, Jeltsch S, et al. Six networks on a universal neuromorphic computing substrate. Front Neurosci, 2013, 7: 11

    Article  Google Scholar 

  43. Indiveri G, Liu S C. Memory and information processing in neuromorphic systems. Proc IEEE, 2015, 103: 1379–1397

    Article  Google Scholar 

  44. Alibart F, Zamanidoost E, Strukov D B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat Commun, 2013, 4: 2072

    Article  Google Scholar 

  45. Sheridan P M, Du C, Lu W D. Feature extraction using memristor networks. IEEE Trans Neural Netw Learning Syst, 2016, 27: 2327–2336

    Article  Google Scholar 

  46. Choi S, Sheridan P, Lu W D. Data clustering using memristor networks. Sci Rep, 2015, 5: 10492

    Article  Google Scholar 

  47. Sheridan P, Ma W, Lu W. Pattern recognition with memristor networks. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, 2014. 1078–1081

    Google Scholar 

  48. Adhikari S P, Yang C J, Kim H, et al. Memristor bridge synapse-based neural network and its learning. IEEE Trans Neural Netw Learning Syst, 2012, 23: 1426–1435

    Article  Google Scholar 

  49. Hu M, Strachan J P, Grafals E M, et al. Dot-product engine for neuromorphic computing. In: Proceedings of the 53rd Annual Design Automation Conference, Austin, 2016

    Google Scholar 

  50. Choi S, Shin J H, Lee J, et al. Experimental demonstration of feature extraction and dimensionality reduction using memristor networks. Nano Lett, 2017, 17: 3113–3118

    Article  Google Scholar 

  51. Yu S, Chen P Y, Cao Y, et al. Scaling-up resistive synaptic arrays for neuro-inspired architecture: challenges and prospect. In: Proceedings of International Electron Devices Meeting, Washington, 2015

    Google Scholar 

  52. Sheridan P, Lu W D. Defect consideratons for robust sparse coding using memristor arrays. In: Proceedings of the 2015 IEEE/ACM International Symposium on Nanoscale Architectures, Boston, 2015, 137–138

    Chapter  Google Scholar 

  53. Ma W, Cai F, Du C, et al. Device nonideality effects on image reconstruction using memristor arrays. In: Proceedings of 2016 IEEE International Electron Devices Meeting (IEDM), San Francisco, 2016

    Google Scholar 

  54. 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 

  55. Tuma T, Pantazi A, Le Gallo M, et al. Stochastic phase-change neurons. Nat Nanotech, 2016, 11: 693–699

    Article  Google Scholar 

  56. Chen B, Cai F X, Zhou J T, et al. Efficient in-memory computing architecture based on crossbar arrays. In: Proceedings of International Electron Devices Meeting, Washington, 2015

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Science Foundation (NSF) (Grant Nos. ECCS-1708700, CCF-1617315). We would like to thank F CAI, J LEE and J SHIN for helpful discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei D. Lu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, W., Zidan, M.A. & Lu, W.D. Neuromorphic computing with memristive devices. Sci. China Inf. Sci. 61, 060422 (2018). https://doi.org/10.1007/s11432-017-9424-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-017-9424-y

Keywords

Navigation