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Neuromorphic spintronics

Abstract

Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial neurons and synapses are, however, currently limited by the energy and area requirements of these components. Spintronic nanodevices, which exploit both the magnetic and electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits, and magnetic tunnel junctions are of particular interest as neuromorphic computing elements because they are compatible with standard integrated circuits and can support multiple functionalities. Here, we review the development of spintronic devices for neuromorphic computing. We examine how magnetic tunnel junctions can serve as synapses and neurons, and how magnetic textures, such as domain walls and skyrmions, can function as neurons. We also explore spintronics-based implementations of neuromorphic computing tasks, such as pattern recognition in an associative memory, and discuss the challenges that exist in scaling up these systems.

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Fig. 1: Magnetic tunnel junctions for memory applications.
Fig. 2: Spintronic-based memristors.
Fig. 3: Neuromorphic computing with spin torque nano-oscillators.
Fig. 4: Computing with stochastic magnetic tunnel junctions.
Fig. 5: Magnetic skyrmions.

References

  1. 1.

    Big data needs a hardware revolution. Nature 554, 145–146 (2018).

  2. 2.

    Furber, S. Large-scale neuromorphic computing systems. J. Neural Eng. 13, 051001 (2016).

    Google Scholar 

  3. 3.

    Indiveri, G. et al. Neuromorphic silicon neuron circuits. Neuromorphic Eng. 5, 73 (2011).

    Google Scholar 

  4. 4.

    Locatelli, N., Cros, V. & Grollier, J. Spin-torque building blocks. Nat. Mater. 13, 11–20 (2014).

    Google Scholar 

  5. 5.

    Grollier, J., Querlioz, D. & Stiles, M. D. Spintronic nanodevices for bioinspired computing. Proc. IEEE 104, 2024–2039 (2016).

    Google Scholar 

  6. 6.

    Schuman, C. D. et al. A survey of neuromorphic computing and neural networks in hardware. Preprint at https://arxiv.org/abs/1705.06963 (2017).

  7. 7.

    Chung, S. W. et al. 4Gbit density STT-MRAM using perpendicular MTJ realized with compact cell structure. In 2016 IEEE Int. Electron Devices Meeting (IEDM) 27.1.1–27.1.4 (IEEE, 2016).

  8. 8.

    Jarollahi, H. et al. A nonvolatile associative memory-based context-driven search engine using 90 nm CMOS/MTJ-hybrid logic-in-memory architecture. IEEE J. Emerg. Sel. Top. Circuits Syst. 4, 460–474 (2014).

    Google Scholar 

  9. 9.

    Ma, Y. et al. A 600-μW ultra-low-power associative processor for image pattern recognition employing magnetic tunnel junction-based nonvolatile memories with autonomic intelligent power-gating scheme. Jpn. J. Appl. Phys. 55, 04EF15 (2016).

    Google Scholar 

  10. 10.

    Zhou, P., Zhao, B., Yang, J. & Zhang, Y. Energy reduction for STT-RAM using early write termination. In 2009 IEEE/ACM Int. Conference on Computer-Aided Design - Digest of Technical Papers 264–268 (IEEE, 2009).

  11. 11.

    Faisal, A. A., Selen, L. P. J. & Wolpert, D. M. Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008).

    Google Scholar 

  12. 12.

    Bottou, L. & Bousquet, O. The tradeoffs of large scale learning. In Proc. 20th Int. Conference on Neural Information Processing Systems 161–168 (Curran Associates, 2007).

  13. 13.

    Locatelli, N., Vincent, A. F. & Querlioz, D. Use of magnetoresistive random-access memory as approximate memory for training neural networks. In 25th IEEE Int. Conference on Electronics, Circuits and Systems (ICECS) 553–556 (IEEE, 2018).

  14. 14.

    Senn, W. & Fusi, S. Convergence of stochastic learning in perceptrons with binary synapses. Phys. Rev. E 71, 061907 (2005).

    Google Scholar 

  15. 15.

    Bill, J. & Legenstein, R. A compound memristive synapse model for statistical learning through STDP in spiking neural networks. Neuromorphic Eng. 8, 412 (2014).

    Google Scholar 

  16. 16.

    Vincent, A. F. et al. Spin-transfer torque magnetic memory as a stochastic memristive synapse for neuromorphic systems. IEEE Trans. Biomed. Circuits Syst. 9, 166–174 (2015).

    Google Scholar 

  17. 17.

    Widrow, B. An Adaptive ‘Adaline’ Neuron Using Chemical ‘Memistors’ (Stanford Electronics Laboratories, 1960).

  18. 18.

    Chua, L. Memristor-the missing circuit element. IEEE Trans. Circuit Theory 18, 507–519 (1971).

    Google Scholar 

  19. 19.

    Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–83 (2008).

    Google Scholar 

  20. 20.

    Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).

    Google Scholar 

  21. 21.

    Likharev, K. K. CrossNets: neuromorphic hybrid CMOS/nanoelectronic networks. Sci. Adv. Mater. 3, 322–331 (2011).

    Google Scholar 

  22. 22.

    Sharad, M., Augustine, C., Panagopoulos, G. & Roy, K. Spin-based neuron model with domain-wall magnets as synapse. IEEE Trans. Nanotechnol. 11, 843–853 (2012).

    Google Scholar 

  23. 23.

    Wang, X., Chen, Y., Xi, H., Li, H. & Dimitrov, D. Spintronic memristor through spin-torque-induced magnetization motion. IEEE Electron Device Lett. 30, 294–297 (2009).

    Google Scholar 

  24. 24.

    Yamaguchi, A. et al. Real-space observation of current-driven domain wall motion in submicron magnetic wires. Phys. Rev. Lett. 92, 077205 (2004).

    Google Scholar 

  25. 25.

    Grollier, J. et al. Switching a spin valve back and forth by current-induced domain wall motion. Appl. Phys. Lett. 83, 509–511 (2003).

    Google Scholar 

  26. 26.

    Chanthbouala, A. et al. Vertical-current-induced domain-wall motion in MgO-based magnetic tunnel junctions with low current densities. Nat. Phys. 7, 626–630 (2011).

    Google Scholar 

  27. 27.

    Lequeux, S. et al. A magnetic synapse: multilevel spin-torque memristor with perpendicular anisotropy. Sci. Rep. 6, 31510 (2016).

    Google Scholar 

  28. 28.

    Huang, Y., Kang, W., Zhang, X., Zhou, Y. & Zhao, W. Magnetic skyrmion-based synaptic devices. Nanotechnology 28, 08LT02 (2017).

    Google Scholar 

  29. 29.

    Wadley, P. et al. Electrical switching of an antiferromagnet. Science 351, 587–590 (2016).

    Google Scholar 

  30. 30.

    Grzybowski, M. J. et al. Imaging current-induced switching of antiferromagnetic domains in CuMnAs. Phys. Rev. Lett. 118, 057701 (2017).

    Google Scholar 

  31. 31.

    Miron, I. M. et al. Perpendicular switching of a single ferromagnetic layer induced by in-plane current injection. Nature 476, 189–193 (2011).

    Google Scholar 

  32. 32.

    Liu, L. et al. Spin-torque switching with the giant spin Hall effect of tantalum. Science 336, 555–558 (2012).

    Google Scholar 

  33. 33.

    Fukami, S., Anekawa, T., Zhang, C. & Ohno, H. A spin–orbit torque switching scheme with collinear magnetic easy axis and current configuration. Nat. Nanotechnol. 11, 621–625 (2016).

    Google Scholar 

  34. 34.

    Fukami, S., Zhang, C., DuttaGupta, S., Kurenkov, A. & Ohno, H. Magnetization switching by spin-orbit torque in an antiferromagnet-ferromagnet bilayer system. Nat. Mater. 15, 535–541 (2016).

    Google Scholar 

  35. 35.

    Kurenkov, A., Zhang, C., DuttaGupta, S., Fukami, S. & Ohno, H. Device-size dependence of field-free spin-orbit torque induced magnetization switching in antiferromagnet/ferromagnet structures. Appl. Phys. Lett. 110, 092410 (2017).

    Google Scholar 

  36. 36.

    Hoppensteadt, F. C. & Izhikevich, E. M. Oscillatory neurocomputers with dynamic connectivity. Phys. Rev. Lett. 82, 2983–2986 (1999).

    Google Scholar 

  37. 37.

    Engel, A. K., Fries, P. & Singer, W. Dynamic predictions: oscillations and synchrony in top–down processing. Nat. Rev. Neurosci. 2, 704–716 (2001).

    Google Scholar 

  38. 38.

    Buzsaki, G. Rhythms of the Brain (Oxford Univ. Press, 2011).

  39. 39.

    Gerstner, W., Kistler, W. M., Naud, R. & Paninski, L. Neuronal Dynamics (Cambridge Univ. Press, 2014).

  40. 40.

    Kiselev, S. I. et al. Microwave oscillations of a nanomagnet driven by a spin-polarized current. Nature 425, 380–383 (2003).

    Google Scholar 

  41. 41.

    Rippard, W. H., Pufall, M. R., Kaka, S., Russek, S. E. & Silva, T. J. Direct-current induced dynamics in Co90Fe10/Ni80Fe20 point contacts. Phys. Rev. Lett. 92, 027201 (2004).

    Google Scholar 

  42. 42.

    Sengupta, A., Panda, P., Wijesinghe, P., Kim, Y. & Roy, K. Magnetic tunnel junction mimics stochastic cortical spiking neurons. Sci. Rep. 6, 30039 (2016).

    Google Scholar 

  43. 43.

    Tsunegi, S. et al. Evaluation of memory capacity of spin torque oscillator for recurrent neural networks. Jpn. J. Appl. Phys. 57, 120307 (2018).

    Google Scholar 

  44. 44.

    Slavin, A. & Tiberkevich, V. Nonlinear auto-oscillator theory of microwave generation by spin-polarized current. IEEE Trans. Magn. 45, 1875–1918 (2009).

    Google Scholar 

  45. 45.

    Kaka, S. et al. Mutual phase-locking of microwave spin torque nano-oscillators. Nature 437, 389–392 (2005).

    Google Scholar 

  46. 46.

    Mancoff, F. B., Rizzo, N. D., Engel, B. N. & Tehrani, S. Phase-locking in double-point-contact spin-transfer devices. Nature 437, 393–395 (2005).

    Google Scholar 

  47. 47.

    Houshang, A. et al. Spin-wave-beam driven synchronization of nanocontact spin-torque oscillators. Nat. Nanotechnol. 11, 280–286 (2016).

    Google Scholar 

  48. 48.

    Locatelli, N. et al. Efficient synchronization of dipolarly coupled vortex-based spin transfer nano-oscillators. Sci. Rep. 5, 17039 (2015).

    Google Scholar 

  49. 49.

    Awad, A. A. et al. Long-range mutual synchronization of spin Hall nano-oscillators. Nat. Phys. 13, 292–299 (2017).

    Google Scholar 

  50. 50.

    Lebrun, R. et al. Mutual synchronization of spin torque nano-oscillators through a long-range and tunable electrical coupling scheme. Nat. Commun. 8, 15825 (2017).

    Google Scholar 

  51. 51.

    Pufall, M. R. et al. Physical implementation of coherently coupled oscillator networks. IEEE J. Explor. Solid-State Comput. Devices Circuits 1, 76–84 (2015).

    Google Scholar 

  52. 52.

    Yogendra, K., Fan, D., Jung, B. & Roy, K. Magnetic pattern recognition using injection-locked spin-torque nano-oscillators. IEEE Trans. Electron Devices 63, 1674–1680 (2016).

    Google Scholar 

  53. 53.

    Fell, J. & Axmacher, N. The role of phase synchronization in memory processes. Nat. Rev. Neurosci. 12, 105–118 (2011).

    Google Scholar 

  54. 54.

    Torrejon, J. et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature 547, 428–431 (2017).

    Google Scholar 

  55. 55.

    Riou, M. et al. Neuromorphic computing through time-multiplexing with a spin-torque nano-oscillator. In 2017 IEEE Int. Electron Devices Meeting (IEDM) 36.3.1–36.3.4 (IEEE, 2017).

  56. 56.

    Appeltant, L. et al. Information processing using a single dynamical node as complex system. Nat. Commun. 2, 468 (2011).

    Google Scholar 

  57. 57.

    Liberman, M. et al. TI-46 Word LDC93S9 (Linguistic Data Consortium, 1993); https://catalog.ldc.upenn.edu/LDC93S9

  58. 58.

    Stein, R. B., Gossen, E. R. & Jones, K. E. Neuronal variability: noise or part of the signal? Nat. Rev. Neurosci. 6, 389–397 (2005).

    Google Scholar 

  59. 59.

    Conrad, M., Engl, E. & Jolivet, R. B. Energy use constrains brain information processing. In 2017 IEEE Int. Electron Devices Meeting (IEDM) 11.3.1–11.3.3 (IEEE, 2017).

  60. 60.

    Pouget, A., Dayan, P. & Zemel, R. Information processing with population codes. Nat. Rev. Neurosci. 1, 125–132 (2000).

    Google Scholar 

  61. 61.

    Vodenicarevic, D. et al. Low-energy truly random number generation with superparamagnetic tunnel junctions for unconventional computing. Phys. Rev. Appl. 8, 054045 (2017).

    Google Scholar 

  62. 62.

    Locatelli, N. et al. Noise-enhanced synchronization of stochastic magnetic oscillators. Phys. Rev. Appl. 2, 034009 (2014).

    Google Scholar 

  63. 63.

    Mizrahi, A. et al. Controlling the phase locking of stochastic magnetic bits for ultra-low power computation. Sci. Rep. 6, 30535 (2016).

    Google Scholar 

  64. 64.

    Camsari, K. Y., Faria, R., Sutton, B. M. & Datta, S. Stochastic p-bits for invertible logic. Phys. Rev. X 7, 031014 (2017).

    Google Scholar 

  65. 65.

    Camsari, K. Y., Salahuddin, S. & Datta, S. Implementing p-bits with embedded MTJ. IEEE Electron Device Lett. 38, 1767–1770 (2017).

    Google Scholar 

  66. 66.

    Pufall, M. R. et al. Large-angle, gigahertz-rate random telegraph switching induced by spin-momentum transfer. Phys. Rev. B 69, 214409 (2004).

    Google Scholar 

  67. 67.

    Fábián, A. et al. Current-induced two-level fluctuations in pseudo-spin-valve (Co/Cu/Co) nanostructures. Phys. Rev. Lett. 91, 257209 (2003).

    Google Scholar 

  68. 68.

    Parks, B. et al. Superparamagnetic perpendicular magnetic tunnel junctions for true random number generators. AIP Adv. 8, 055903 (2017).

    Google Scholar 

  69. 69.

    Yamanouchi, M., Chiba, D., Matsukura, F. & Ohno, H. Current-induced domain-wall switching in a ferromagnetic semiconductor structure. Nature 428, 539–542 (2004).

    Google Scholar 

  70. 70.

    Thomas, L., Moriya, R., Rettner, C. & Parkin, S. S. P. Dynamics of magnetic domain walls under their own inertia. Science 330, 1810–1813 (2010).

    Google Scholar 

  71. 71.

    Woo, S. et al. Observation of room-temperature magnetic skyrmions and their current-driven dynamics in ultrathin metallic ferromagnets. Nat. Mater. 15, 501–506 (2016).

    Google Scholar 

  72. 72.

    Allwood, D. A. et al. Magnetic domain-wall logic. Science 309, 1688–1692 (2005).

    Google Scholar 

  73. 73.

    Fernández-Pacheco, A. et al. Three-dimensional nanomagnetism. Nat. Commun. 8, 15756 (2017).

    Google Scholar 

  74. 74.

    Hayashi, M. et al. Dependence of current and field driven depinning of domain walls on their structure and chirality in permalloy nanowires. Phys. Rev. Lett. 97, 207205 (2006).

    Google Scholar 

  75. 75.

    Hayward, T. J. Intrinsic nature of stochastic domain wall pinning phenomena in magnetic nanowire devices. Sci. Rep. 5, 13279 (2015).

    Google Scholar 

  76. 76.

    Zázvorka, J. et al. Thermal skyrmion diffusion used in a reshuffler device. Nat. Nanotechnol. 14, 658–661 (2019).

    Google Scholar 

  77. 77.

    Pinna, D. et al. Skyrmion gas manipulation for probabilistic computing. Phys. Rev. Appl. 9, 064018 (2018).

    Google Scholar 

  78. 78.

    Li, S. et al. Magnetic skyrmion-based artificial neuron device. Nanotechnology 28, 31LT01 (2017).

    Google Scholar 

  79. 79.

    Chen, X. et al. A compact skyrmionic leaky–integrate–fire spiking neuron device. Nanoscale 10, 6139–6146 (2018).

    Google Scholar 

  80. 80.

    Du, H. et al. Electrical probing of field-driven cascading quantized transitions of skyrmion cluster states in MnSi nanowires. Nat. Commun. 6, 7637 (2015).

    Google Scholar 

  81. 81.

    Prychynenko, D. et al. Magnetic skyrmion as a nonlinear resistive element: a potential building block for reservoir computing. Phys. Rev. Appl. 9, 014034 (2018).

    Google Scholar 

  82. 82.

    Bourianoff, G., Pinna, D., Sitte, M. & Everschor-Sitte, K. Potential implementation of reservoir computing models based on magnetic skyrmions. AIP Adv. 8, 055602 (2018).

    Google Scholar 

  83. 83.

    Pinna, D., Bourianoff, G. & Everschor-Sitte, K. Reservoir computing with random skyrmion textures. Preprint at https://arxiv.org/abs/1811.12623 (2018).

  84. 84.

    Hanneken, C. et al. Electrical detection of magnetic skyrmions by tunnelling non-collinear magnetoresistance. Nat. Nanotechnol. 10, 1039–1042 (2015).

    Google Scholar 

  85. 85.

    Kubetzka, A., Hanneken, C., Wiesendanger, R. & von Bergmann, K. Impact of the skyrmion spin texture on magnetoresistance. Phys. Rev. B 95, 104433 (2017).

    Google Scholar 

  86. 86.

    Krüger, B. Current-Driven Magnetization Dynamics: Analytical Modeling and Numerical Simulation. PhD thesis, University of Hamburg (2011).

  87. 87.

    Prezioso, M. et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature 521, 61–64 (2015).

    Google Scholar 

  88. 88.

    Burr, G. W. et al. Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: comparative performance analysis (accuracy, speed, and power). In 2015 IEEE Int. Electron Devices Meeting (IEDM) 4.4.1-4.4.4 (2015).

  89. 89.

    Borders, WilliamA. et al. Analogue spin–orbit torque device for artificial-neural-network-based associative memory operation. Appl. Phys. Express 10, 013007 (2016).

    Google Scholar 

  90. 90.

    Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).

    MathSciNet  MATH  Google Scholar 

  91. 91.

    Ackley, D. H., Hinton, G. E. & Sejnowski, T. J. A learning algorithm for Boltzmann machines. Cogn. Sci. 9, 147–169 (1985).

    Google Scholar 

  92. 92.

    Romera, M. et al. Vowel recognition with four coupled spin-torque nano-oscillators. Nature 563, 230–234 (2018).

    Google Scholar 

  93. 93.

    Vodenicarevic, D., Locatelli, N., Araujo, F. A., Grollier, J. & Querlioz, D. A nanotechnology-ready computing scheme based on a weakly coupled oscillator network. Sci. Rep. 7, 44772 (2017).

    Google Scholar 

  94. 94.

    Vodenicarevic, D., Locatelli, N., Grollier, J. & Querlioz, D. Nano-oscillator-based classification with a machine learning-compatible architecture. J. Appl. Phys. 124, 152117 (2018).

    Google Scholar 

  95. 95.

    Vogel, M. et al. Phase programming in coupled spintronic oscillators. Preprint at https://arxiv.org/abs/1811.02154 (2018).

  96. 96.

    Mizrahi, A. et al. Neural-like computing with populations of superparamagnetic basis functions. Nat. Commun. 9, 1533 (2018).

    Google Scholar 

  97. 97.

    Mizrahi, A., Grollier, J., Querlioz, D. & Stiles, M. D. Overcoming device unreliability with continuous learning in a population coding based computing system. J. Appl. Phys. 124, 152111 (2018).

    Google Scholar 

  98. 98.

    Sutton, B., Camsari, K. Y., Behin-Aein, B. & Datta, S. Intrinsic optimization using stochastic nanomagnets. Sci. Rep. 7, 44370 (2017).

    Google Scholar 

  99. 99.

    Behin-Aein, B., Diep, V. & Datta, S. A building block for hardware belief networks. Sci. Rep. 6, 29893 (2016).

    Google Scholar 

  100. 100.

    Behin-Aein, B. Computing multi-magnet based devices and methods for solution of optimization problems. (2014).

  101. 101.

    Zand, R. et al. Low-energy deep belief networks using intrinsic sigmoidal spintronic-based probabilistic neurons. In Proc. 2018 Great Lakes Symposium on VLSI 15–20 (ACM, 2018).

  102. 102.

    Farhi, E. et al. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292, 472–475 (2001).

    MathSciNet  MATH  Google Scholar 

  103. 103.

    Peng, X. et al. Quantum adiabatic algorithm for factorization and its experimental implementation. Phys. Rev. Lett. 101, 220405 (2008).

    Google Scholar 

  104. 104.

    Faria, R., Camsari, K. Y. & Datta, S. Low-barrier nanomagnets as p-bits for spin logic. IEEE Magn. Lett. 8, 1–5 (2017).

    Google Scholar 

  105. 105.

    Camsari, K. Y., Chowdhury, S. & Datta, S. Scalable emulation of sign-problem—free Hamiltonians with room-temperature p-bits. Phys. Rev. Appl. 12, 034061 (2019).

    Google Scholar 

  106. 106.

    Johnson, M. W. et al. Quantum annealing with manufactured spins. Nature 473, 194–198 (2011).

    Google Scholar 

  107. 107.

    Troyer, M. & Wiese, U.-J. Computational complexity and fundamental limitations to fermionic quantum monte carlo simulations. Phys. Rev. Lett. 94, 170201 (2005).

    Google Scholar 

  108. 108.

    Bhanja, S., Karunaratne, D. K., Panchumarthy, R., Rajaram, S. & Sarkar, S. Non-Boolean computing with nanomagnets for computer vision applications. Nat. Nanotechnol. 11, 177–183 (2016).

    Google Scholar 

  109. 109.

    Debashis, P. et al. Experimental demonstration of nanomagnet networks as hardware for Ising computing. In 2016 IEEE Int. Electron Devices Meeting (IEDM) 34.3.1–34.3.4 (IEEE, 2016).

  110. 110.

    Nomura, H. et al. Reservoir computing with dipole-coupled nanomagnets. Jpn. J. Appl. Phys. 58, 070901 (2019).

    Google Scholar 

  111. 111.

    Jensen, J. H., Folven, E. & Tufte, G. Computation in artificial spin ice. In ALIFE 2018: The 2018 Conference on Artificial Life https://doi.org/10.1162/isal_a_00011 (MIT Press, 2018).

  112. 112.

    Xu, X. et al. Scaling for edge inference of deep neural networks. Nat. Electron. 1, 216–222 (2018).

    Google Scholar 

  113. 113.

    Khvalkovskiy, A. V. et al. Basic principles of STT-MRAM cell operation in memory arrays. J. Phys. Appl. Phys. 46, 074001 (2013).

    Google Scholar 

  114. 114.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Google Scholar 

  115. 115.

    Ambrogio, S. et al. Equivalent-accuracy accelerated neural-network training using analogue memory. Nature 558, 60–67 (2018).

    Google Scholar 

  116. 116.

    Nagaosa, N. & Tokura, Y. Topological properties and dynamics of magnetic skyrmions. Nat. Nanotechnol. 8, 899–911 (2013).

    Google Scholar 

  117. 117.

    Wu, S., Li, G., Chen, F. & Shi, L. Training and inference with integers in deep neural networks. Preprint at https://arxiv.org/abs/1802.04680 (2018).

  118. 118.

    Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R. & Bengio, Y. Quantized neural networks: training neural networks with low precision weights and activations. J. Mach. Learn. Res. 18, 1 (2017).

    MathSciNet  MATH  Google Scholar 

  119. 119.

    Rastegari, M., Ordonez, V., Redmon, J. & Farhadi, A. Xnor-net: imagenet classification using binary convolutional neural networks. In European Conf. on Computer Vision 525–542 (Springer, 2016).

  120. 120.

    Mellnik, A. R. et al. Spin-transfer torque generated by a topological insulator. Nature 511, 449–451 (2014).

    Google Scholar 

  121. 121.

    Chakravarty, A. et al. Supervised learning of an opto-magnetic neural network with ultrashort laser pulses. Appl. Phys. Lett. 114, 192407 (2018).

    Google Scholar 

  122. 122.

    Davies, C. S. et al. Towards massively parallelized all-optical magnetic recording. J. Appl. Phys. 123, 213904 (2018).

    Google Scholar 

  123. 123.

    Khymyn, R. et al. Ultra-fast artificial neuron: generation of picosecond-duration spikes in a current-driven antiferromagnetic auto-oscillator. Sci. Rep. 8, 15727 (2018).

    Google Scholar 

  124. 124.

    Sulymenko, O. et al. Ultra-fast logic devices using artificial “neurons” based on antiferromagnetic pulse generators. J. Appl. Phys. 124, 152115 (2018).

    Google Scholar 

  125. 125.

    Sato, H. et al. Properties of magnetic tunnel junctions with a MgO/CoFeB/Ta/CoFeB/MgO recording structure down to junction diameter of 11 nm. Appl. Phys. Lett. 105, 062403 (2014).

    Google Scholar 

  126. 126.

    Piraux, L. et al. Giant magnetoresistance in magnetic multilayered nanowires. Appl. Phys. Lett. 65, 2484–2486 (1994).

    Google Scholar 

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Acknowledgements

Work by M.D.S. was supported by the Quantum Materials for Energy Efficient Neuromorphic Computing, an Energy Frontier Research Center funded by DOE, Office of Science, BES under award no. DE-SC0019273. S.F. is funded by JSPS Grant-in-Aid for Scientific Research 19H05622 and JST-OPERA JPMJOP1611. K.E.S. is funded by the German Research Foundation (DFG) under the Project No. EV 196/2-1 and acknowledges support through the Emergent AI Center, funded by the Carl-Zeiss-Stiftung. Work by J.G. was supported by the European Research Council ERC under Grant bioSPINspired 682955. Work by D.Q. was supported by the European Research Council grant NANOINFER (reference: 715872). S.F. acknowledges discussion with H. Ohno. K.E.S. acknowledges discussions with D. Pinna. K.Y.C. acknowledges useful discussions with S. Datta.

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Grollier, J., Querlioz, D., Camsari, K.Y. et al. Neuromorphic spintronics. Nat Electron 3, 360–370 (2020). https://doi.org/10.1038/s41928-019-0360-9

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