Spintronic Nanodevices for Bioinspired Computing

Proceedings of the IEEE

Volumn 104, Issue 10, 2016, Pages 2024-2039

  Grollier, Julie ^a   Querlioz, Damien ^b   Stiles, Mark D ^c  

^a UNITÉ MIXTE DE PHYSIQUE CNRS THALES   (France)

^b CNRS   (France)

^c NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY   (United States)

Author keywords

Bioinspired computing; magnetic tunnel junctions (MTJs); spintronics

Indexed keywords

CMOS INTEGRATED CIRCUITS; COMPLEX NETWORKS; CONTROL SYSTEM SYNTHESIS; INFORMATION MANAGEMENT; MEMORY ARCHITECTURE; METALS; MOS DEVICES; NANOSTRUCTURED MATERIALS; OXIDE SEMICONDUCTORS; SEMICONDUCTOR JUNCTIONS; SPINTRONICS; TUNNEL JUNCTIONS;

AUTOMATIC CLASSIFICATION; BIO-INSPIRED ARCHITECTURES; BIO-INSPIRED COMPUTING; COMPLEMENTARY METAL OXIDE SEMICONDUCTORS; DIFFERENT OPERATING CONDITIONS; MAGNETIC TUNNEL JUNCTION; NANOMETER SCALE DEVICES; SPINTRONIC NANODEVICES;

MAGNETIC DEVICES;

EID: 85015871661     PISSN: 00189219     EISSN: 15582256     Source Type: Journal    
DOI: 10.1109/JPROC.2016.2597152     Document Type: Article

References (147)

1. Y. LeCun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, May 2015.
2. R. D. Hof, "Deep learning," MIT Technol. Rev., 2013. [Online]. Available: https://www.technologyreview.com/s/513696/deeplearning/
3. W. Knight, "Deep learning catches on in new industries, from fashion to finance," MIT Technol. Rev.,May 2015. [Online]. Available: https://www.technologyreview.com/s/ 537806/deep-learning-catches-on-in-newindustries- from-fashion-to-finance/
4. E. Gelenbe and Y. Caseau, "The impact of information technology on energy consumption and carbon emissions," Ubiquity, vol. 2015, pp. 1:1-1:15, Jun. 2015.
5. M. Fischetti, "Computers versus brains," Sci. Amer., Nov. 2011. [Online]. Available: http://www.scientificamerican. com/article/computers-vs-brains/
6. B. Sengupta and M. B. Stemmler, "Power consumption during neuronal computation," Proc. IEEE, vol. 102, no. 5, pp. 738-750, May 2014.
7. G. Indiveri and S. C. Liu, "Memory and information processing in neuromorphic systems," Proc. IEEE, vol. 103, no. 8, pp. 1379-1397, Aug. 2015.
8. P. A. Merolla et al., "A million spiking-neuron integrated circuit with a scalable communication network and interface," Science, vol. 345, no. 6197, pp. 668-673, Aug. 2014.
9. B. V. Benjamin et al., "Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations," Proc. IEEE, vol. 102, no. 5, pp. 699-716, May 2014.
10. S. B. Furber, F. Galluppi, S. Temple, and L. A. Plana, "The SpiNNaker project," Proc. IEEE, vol. 102, no. 5, pp. 652-665, May 2014.
11. D. Querlioz, O. Bichler, A. F. Vincent, and C. Gamrat, "Bioinspired programming of memory devices for implementing an inference engine," Proc. IEEE, vol. 103, no. 8, pp. 1398-1416, Aug. 2015.
12. K. Meier, "A mixed-signal universal neuromorphic computing system," in Proc. IEEE Int. Electron Devices Meet., 2015, pp. 4.6.1-4.6.4.
13. R. Colin Johnson, "Neuromorphic chip market to rise," EETimes, Sept. 2015. [Online]. Available: http://www.eetimes.com/document.asp?doc-id=1327791
14. D. B. Strukov, G. S. Snider, D. R. Stewart, and R. S. Williams, "The missing memristor found," Nature, vol. 453, no. 7191, pp. 80-83, May 2008.
15. G. S. Snider, "Self-organized computation with unreliable, memristive nanodevices," Nanotechnology, vol. 18, no. 36, 2007, Art. no. 365202.
16. S. H. Jo et al., "Nanoscale memristor device as synapse in neuromorphic systems," Nano Lett., vol. 10, no. 4, pp. 1297-1301, Apr. 2010.
17. J. J. Yang, D. B. Strukov, and D. R. Stewart, "Memristive devices for computing," Nature Nanotechnol., vol. 8, no. 1, pp. 13-24, Jan. 2013.
18. D. Kuzum, S. Yu, and H.-S. P. Wong, "Synaptic electronics: Materials, devices and applications," Nanotechnology, vol. 24, no. 38, 2013, Art. no. 382001.
19. S. Saïghi et al., "Plasticity in memristive devices for spiking neural networks," Neuromorphic Eng., vol. 9, p. 51, 2015.
20. F. Alibart, E. Zamanidoost, and D. B. Strukov, "Pattern classification by memristive crossbar circuits using ex situ and in situ training," Nature Commun., vol. 4, p. 2072, Jun. 2013.
21. S. Park et al., "Electronic system with memristive synapses for pattern recognition," Sci. Rep., vol. 5, p. 10 123, May 2015.
22. G. W. Burr et al., "Experimental demonstration and tolerancing of a large-scale neural network (165 000 synapses), using phase-change memory as the synaptic weight element," in Proc. IEEE Int. Electron Devices Meet., 2014, pp. 29.5.1-29.5.4.
23. D. R. Chialvo, "Emergent complex neural dynamics," Nature Phys., vol. 6, no. 10, pp. 744-750, Oct. 2010.
24. J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities," Proc. Nat. Acad. Sci., vol. 79, no. 8, pp. 2554-2558, Jan. 1982.
25. D. J. Amit, H. Gutfreund, and H. Sompolinsky, "Storing infinite numbers of patterns in a spin-glass model of neural networks," Phys. Rev. Lett., vol. 55, no. 14, pp. 1530-1533, Sep. 1985.
26. Y. Ma and C. Gong, "Asymmetric Sherrington-Kirkpatrick model of neural networks with random neuronal threshold," Phys. Rev. B, vol. 46, no. 6, pp. 3436-3440, Aug. 1992.
27. H. Jaeger and H. Haas, "Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication," Science, vol. 304, no. 5667, pp. 78-80, Apr. 2004.
28. R. Borisyuk, M. Denham, F. Hoppensteadt, Y. Kazanovich, and O. Vinogradova, "An oscillatory neural network model of sparse distributed memory and novelty detection," Biosystems, vol. 58, no. 1-3, pp. 265-272, Dec. 2000.
29. F. C. Hoppensteadt and E. M. Izhikevich, "Oscillatory neurocomputers with dynamic connectivity," Phys. Rev. Lett., vol. 82, no. 14, pp. 2983-2986, Apr. 1999.
30. R. W. Hölzel and K. Krischer, "Pattern recognition with simple oscillating circuits," New J. Phys., vol. 13, no. 7, p. 73 031, 2011.
31. M. N. Baibich et al., "Giant magnetoresistance of (001)Fe/(001)Cr magnetic superlattices," Phys. Rev. Lett., vol. 61, no. 21, pp. 2472-2475, Nov. 1988.
32. G. Binasch, P. Grünberg, F. Saurenbach, and W. Zinn, "Enhanced magnetoresistance in layered magnetic structures with antiferromagnetic interlayer exchange," Phys. Rev. B, vol. 39, no. 7, pp. 4828-4830, Mar. 1989.
33. M. Julliere, "Tunneling between ferromagnetic films," Phys. Lett. A, vol. 54, no. 3, pp. 225-226, Sep. 1975.
34. S. Yuasa, T. Nagahama, A. Fukushima, Y. Suzuki, and K. Ando, "Giant room-temperature magnetoresistance in single-crystal Fe/MgO/Fe magnetic tunnel junctions," Nature Mater., vol. 3, no. 12, pp. 868-871, Dec. 2004.
35. S. S. P. Parkin et al., "Giant tunnelling magnetoresistance at room temperature with MgO (100) tunnel barriers," Nature Mater., vol. 3, no. 12, pp. 862-867, Dec. 2004.
36. J. C. Slonczewski, "Current-driven excitation of magnetic multilayers," J. Magn. Magn. Mater., vol. 159, no. 1-2, pp. L1-L7, Jun. 1996.
37. L. Berger, "Emission of spin waves by a magnetic multilayer traversed by a current," Phys. Rev. B, vol. 54, no. 13, pp. 9353-9358, Oct. 1996.
38. B. Dieny et al., "Magnetotransport properties of magnetically soft spin-valve structures (invited)," J. Appl. Phys., vol. 69, no. 8, pp. 4774-4779, Apr. 1991.
39. H. Sato et al., "Perpendicular-anisotropy CoFeB-MgO magnetic tunnel junctions with a MgO/CoFeB/Ta/CoFeB/MgO recording structure," Appl. Phys. Lett., vol. 101, no. 2, p. 22 414, Jul. 2012.
40. N. Locatelli, V. Cros, and J. Grollier, "Spin-torque building blocks," Nature Mater., vol. 13, no. 1, pp. 11-20, Jan. 2014.
41. A. V. Khvalkovskiy et al., "Basic principles of STT-MRAM cell operation in memory arrays," J. Phys. Appl. Phys., vol. 46, no. 7, p. 74 001, 2013.
42. G. Hilson, "Everspin aims MRAM at SSD storage tiers," EETimes, Apr. 2016. [Online]. Available: http://www.eetimes.com/document.asp?doc-id=1329477
43. K. Lee, J. J. Kan, and S. H. Kang, "Unified embedded non-volatile memory for emerging mobile markets," in Proc. IEEE/ ACM Int. Symp. Low Power Electron. Design, 2014, pp. 131-136.
44. C. Layer et al., "Low-power hybrid STT/CMOS system-on-chip embedding non-volatile magnetic memory blocks," in Proc. IEEE 13th Int. New Circuits Syst. Conf., 2015, doi: 10.1109/ NEWCAS.2015.7181999.
45. T. Ohsawa et al., "A 1 Mb nonvolatile embedded memory using 4T2MTJ cell with 32 b fine-grained power gating scheme," IEEE J. Solid-State Circuits, vol. 48, no. 6, pp. 1511-1520, Jun. 2013.
46. N. Sakimura et al., "10.5 A 90 nm 20 MHz fully nonvolatile microcontroller for standby-power-critical applications," in Proc. IEEE Int. Solid-State Circuits Conf. Dig. Tech. Papers, 2014, pp. 184-185.
47. E. Deng et al., "Synchronous 8-bit non-volatile full-adder based on spin transfer torque magnetic tunnel junction," IEEE Trans. Circuits Syst. Reg. Papers, vol. 62, no. 7, pp. 1757-1765, Jul. 2015.
48. T. Hanyu et al., "Challenge of MTJ-based nonvolatile logic-in-memory architecture for ultra low-power and highly dependable VLSI computing," in Proc. IEEE SOI-3DSubthreshold Microelectron. Technol. Unified Conf., 2015, doi: 10.1109/ S3S.2015.7333502.
49. J. A. Katine, F. J. Albert, R. A. Buhrman, E. B. Myers, and D. C. Ralph, "Current-driven magnetization reversal and spin-wave excitations in Co/Cu/Co pillars," Phys. Rev. Lett., vol. 84, no. 14, pp. 3149-3152, Apr. 2000.
50. J. Grollier et al., "Spin-polarized current induced switching in Co/Cu/Co pillars," Appl. Phys. Lett., vol. 78, no. 23, pp. 3663-3665, Jun. 2001.
51. S. Bonetti, P. Muduli, F. Mancoff, and J. Akerman, "Spin torque oscillator frequency versus magnetic field angle: The prospect of operation beyond 65 GHz," Appl. Phys. Lett., vol. 94, no. 10, Mar. 2009, Art. no. 102507.
52. N. Locatelli et al., "Noise-enhanced synchronization of stochastic magnetic oscillators," Phys. Rev. Appl., vol. 2, no. 3, Sep. 2014, Art. no. 34009.
53. X. Wang, Y. Chen, H. Xi, H. Li, and D. Dimitrov, "Spintronic memristor through spin-torque-induced magnetization motion," IEEE Electron Device Lett., vol. 30, no. 3, pp. 294-297, Mar. 2009.
54. A. Chanthbouala et al., "Vertical-currentinduced domain-wall motion in MgO-based magnetic tunnel junctions with low current densities," Nature Phys., vol. 7, no. 8, pp. 626-630, Aug. 2011.
55. A. Slavin and V. Tiberkevich, "Nonlinear auto-oscillator theory of microwave generation by spin-polarized current," IEEE Trans. Magn., vol. 45, no. 4, pp. 1875-1918, Apr. 2009.
56. S. Kaka et al., "Mutual phase-locking of microwave spin torque nano-oscillators," Nature, vol. 437, no. 7057, pp. 389-392, Sep. 2005.
57. F. B. Mancoff, N. D. Rizzo, B. N. Engel, and S. Tehrani, "Phase-locking in double-point-contact spin-transfer devices," Nature, vol. 437, no. 7057, pp. 393-395, Sep. 2005.
58. J. Grollier, V. Cros, and A. Fert, "Synchronization of spin-transfer oscillators driven by stimulated microwave currents," Phys. Rev. B, vol. 73, no. 6, p. 60 409, Feb. 2006.
59. N. Locatelli et al., "Efficient synchronization of dipolarly coupled vortex-based spin transfer nano-oscillators," Sci. Rep., vol. 5, p. 17 039, Nov. 2015.
60. A. Houshang et al., "Spin-wave-beam driven synchronization of nanocontact spin-torque oscillators," Nature Nanotechnol., vol. 11, no. 3, pp. 280-286, Mar. 2016.
61. M. I. Rabinovich, P. Varona, A. I. Selverston, and H. D. I. Abarbanel, "Dynamical principles in neuroscience," Rev. Mod. Phys., vol. 78, no. 4, pp. 1213-1265, Nov. 2006.
62. D. Sussillo, "Neural circuits as computational dynamical systems," Curr. Opin. Neurobiol., vol. 25, pp. 156-163, Apr. 2014.
63. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, Jul. 2006.
64. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," Nature, vol. 323, no. 6088, pp. 533-536, Oct. 1986.
65. D. O. Hebb, The Organization of Behavior: A Neuropsychological Theory. Mahwah, NJ, USA: Psychology Press, 1949.
66. T. Masquelier and S. J. Thorpe, "Unsupervised learning of visual features through spike timing dependent plasticity," PLOS Comput. Biol., vol. 3, no. 2, Feb. 2007, Art. no. e31.
67. Y. K. Chen et al., "Convergence of recognition, mining, and synthesis workloads and its implications," Proc. IEEE, vol. 96, no. 5, pp. 790-807, May 2008.
68. H. Noguchi et al., "A 250-MHz 256b-I/O 1-Mb STT-MRAM with advanced perpendicular MTJ based dual cell for nonvolatile magnetic caches to reduce active power of processors," in Proc. Symp. VLSI Technol., 2013, pp. C108-C109.
69. H. Jarollahi et al., "A nonvolatile associative memory-based context-driven search engine using 90 nm CMOS/ MTJ-hybrid logic-in-memory architecture," IEEE J. Emerging Sel. Top. Circuits Syst., vol. 4, no. 4, pp. 460-474, Dec. 2014.
70. W. Zhao et al., "Synchronous non-volatile logic gate design based on resistive switching memories," IEEE Trans. Circuits Syst. Reg. Papers, vol. 61, no. 2, pp. 443-454, Feb. 2014.
71. N. Locatelli et al., "Spintronic devices as key elements for energy-efficient neuroinspired architectures," in Proc. Design Autom. Test Eur. Conf. Exhibit., 2015, pp. 994-999.
72. E. Kitagawa et al., "Impact of ultra low power and fast write operation of advanced perpendicular MTJ on power reduction for high-performance mobile CPU," in Proc. IEEE IEDM, 2012, pp. 29.4.1-29.4.4.
73. P. Lennie, "The cost of cortical computation," Curr. Biol., vol. 13, no. 6, pp. 493-497, Mar. 2003.
74. R. B. Stein, E. R. Gossen, and K. E. Jones, "Neuronal variability: Noise or part of the signal?" Nature Rev. Neurosci., vol. 6, no. 5, pp. 389-397, May 2005.
75. A. A. Faisal, L. P. J. Selen, and D. M. Wolpert, "Noise in the nervous system," Nature Rev. Neurosci., vol. 9, no. 4, pp. 292-303, Apr. 2008.
76. M. D. McDonnell and L. M. Ward, "The benefits of noise in neural systems: Bridging theory and experiment," Nature Rev. Neurosci., vol. 12, no. 7, pp. 415-426, Jul. 2011.
77. B. B. Averbeck, P. E. Latham, and A. Pouget, "Neural correlations, population coding and computation," Nature Rev. Neurosci., vol. 7, no. 5, pp. 358-366, May 2006.
78. W. Gerstner, W. M. Kistler, R. Naud, and L. Paninski, Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition, 1st ed. Cambridge, U.K.: Cambridge Univ. Press, 2014.
79. C. Mead, "Neuromorphic electronic systems," Proc. IEEE, vol. 78, no. 10, pp. 1629-1636, Oct. 1990.
80. Z. Diao et al., "Spin-transfer torque switching in magnetic tunnel junctions and spin-transfer torque random access memory," J. Phys. Condens. Matter, vol. 19, no. 16, 2007, Art. no. 165209.
81. T. Devolder et al., "Single-shot time-resolved measurements of nanosecond-scale spin-transfer induced switching: Stochastic versus deterministic aspects," Phys. Rev. Lett., vol. 100, no. 5, p. 57 206, Feb. 2008.
82. A. F. Vincent et al., "Analytical macrospin modeling of the stochastic switching time of spin-transfer torque devices," IEEE Trans. Electron Devices, vol. 62, no. 1, pp. 164-170, Jan. 2015.
83. A. Fukushima et al., "Spin dice: A scalable truly random number generator based on spintronics," Appl. Phys. Exp., vol. 7, no. 8, p. 83 001, Aug. 2014.
84. J. H. Lee and K. K. Likharev, "Defect-tolerant nanoelectronic pattern classifiers," Int. J. Circuit Theory Appl., vol. 35, no. 3, pp. 239-264, May 2007.
85. W. Senn and S. Fusi, "Convergence of stochastic learning in perceptrons with binary synapses," Phys. Rev. E, vol. 71, no. 6, p. 61907, Jun. 2005.
86. Y. Kondo and Y. Sawada, "Functional abilities of a stochastic logic neural network," IEEE Trans. Neural Netw., vol. 3, no. 3, pp. 434-443, May 1992.
87. D. S. Modha and S. S. P. Parkin, "Stochastic synapse memory element with spike-timing dependent plasticity (STDP)," U.S. Patent 20100220523 A1, Sep. 2, 2010.
88. H. Markram, J. Lübke, M. Frotscher, and B. Sakmann, "Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs," Science, vol. 275, no. 5297, pp. 213-215, Jan. 1997.
89. G. Bi and M. Poo, "Synaptic modification by correlated activity: Hebb's postulate revisited," Annu. Rev. Neurosci., vol. 24, no. 1, pp. 139-166, 2001.
90. T. Serrano-Gotarredona et al., "STDP and STDP variations with memristors for spiking neuromorphic learning systems," Front. Neurosci., vol. 7, p. 2, 2013.
91. O. Bichler et al., "Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity," Neural Netw., vol. 32, pp. 339-348, Aug. 2012.
92. A. F. Vincent et al., "Spin-transfer torque magnetic memory as a stochastic memristive synapse for neuromorphic systems," IEEE Trans. Biomed. Circuits Syst., vol. 9, no. 2, pp. 166-174, Apr. 2015.
93. J. Bill and R. Legenstein, "A compound memristive synapse model for statistical learning through STDP in spiking neural networks," Neuromorphic Eng., vol. 8, p. 412, 2014.
94. K. Wiesenfeld and F. Moss, "Stochastic resonance and the benefits of noise: From ice ages to crayfish and SQUIDs," Nature, vol. 373, no. 6509, pp. 33-36, Jan. 1995.
95. D. F. Russell, L. A. Wilkens, and F. Moss, "Use of behavioural stochastic resonance by paddle fish for feeding," Nature, vol. 402, no. 6759, pp. 291-294, Nov. 1999.
96. A. Bulsara, E. W. Jacobs, T. Zhou, F. Moss, and L. Kiss, "Stochastic resonance in a single neuron model: Theory and analog simulation," J. Theor. Biol., vol. 152, no. 4, pp. 531-555, Oct. 1991.
97. X. Cheng, C. T. Boone, J. Zhu, and I. N. Krivorotov, "Nonadiabatic stochastic resonance of a nanomagnet excited by spin torque," Phys. Rev. Lett., vol. 105, no. 4, p. 47 202, Jul. 2010.
98. M. D. McDonnell and D. Abbott, "What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology," PLOS Comput. Biol., vol. 5, no. 5, May 2009, Art. no. e1000348.
99. D. Querlioz and V. Trauchessec, "Stochastic resonance in an analog current-mode neuromorphic circuit," in Proc. IEEE Int. Symp. Circuits Syst., 2013, pp. 1596-1599.
100. N. G. Stocks, D. Allingham, and R. P. Morse, "The application of suprathreshold stochastic resonance to cochlear implant coding," Fluct. Noise Lett., vol. 2, no. 3, pp. L169-L181, Sep. 2002.
101. H.-J. Park and K. Friston, "Structural and functional brain networks: From connections to cognition," Science, vol. 342, no. 6158, Nov. 2013, Art. no. 1238411.
102. E. Bullmore and O. Sporns, "Complex brain networks: Graph theoretical analysis of structural and functional systems," Nature Rev. Neurosci., vol. 10, no. 3, pp. 186-198, Mar. 2009.
103. Y. V. Pershin and M. Di Ventra, "Spin memristive systems: Spin memory effects in semiconductor spintronics," Phys. Rev. B, vol. 78, no. 11, Sep. 2008, Art. no. 113309.
104. C. Timm and M. Di Ventra, "Memristive properties of single-molecule magnets," Phys. Rev. B, vol. 86, no. 10, Sep. 2012, Art. no. 104427.
105. J. Grollier, et al., "Magnetic domain wall motion by spin transfer," Comptes Rendus Phys., vol. 12, no. 3, pp. 309-317, Apr. 2011.
106. L. Thomas, R. Moriya, C. Rettner, and S. S. P. Parkin, "Dynamics of magnetic domain walls under their own inertia," Science, vol. 330, no. 6012, pp. 1810-1813, Dec. 2010.
107. J. Münchenberger, G. Reiss, and A. Thomas, "A memristor based on current-induced domain-wall motion in a nanostructured giant magnetoresistance device," J. Appl. Phys., vol. 111, no. 7, Apr. 2012, Art. no. 07D303.
108. S. Lequeux et al., "A magnetic synapse: multilevel spin-torque memristor with perpendicular anisotropy," Sci. Rep., vol. 6, p. 31 510, Aug. 2016.
109. S. Fukami, C. Zhang, S. DuttaGupta, A. Kurenkov, and H. Ohno, "Magnetization switching by spin-orbit torque in an antiferromagnet-ferromagnet bilayer system," Nature Mater., vol. 15, no. 5, pp. 535-541, May 2016.
110. M. Prezioso et al., "Training and operation of an integrated neuromorphic network based on metal-oxide memristors," Nature, vol. 521, no. 7550, pp. 61-64, May 2015.
111. D. Querlioz, O. Bichler, P. Dollfus, and C. Gamrat, "Immunity to device variations in a spiking neural network with memristive nanodevices," IEEE Trans. Nanotechnol., vol. 12, no. 3, pp. 288-295, May 2013.
112. G. Indiveri et al., "Neuromorphic silicon neuron circuits," Neuromorphic Eng., vol. 5, p. 73, 2011.
113. M. Sharad, C. Augustine, G. Panagopoulos, and K. Roy, "Spin-based neuron model with domain-wall magnets as synapse," IEEE Trans. Nanotechnol., vol. 11, no. 4, pp. 843-853, Jul. 2012.
114. K. Roy et al., "Exploring spin transfer torque devices for unconventional computing," IEEE J. Emerging Sel. Top. Circuits Syst., vol. 5, no. 1, pp. 5-16, Mar. 2015.
115. S. G. Ramasubramanian, R. Venkatesan, M. Sharad, K. Roy, and A. Raghunathan, "SPINDLE: SPINtronic Deep Learning Engine for large-scale neuromorphic computing," in Proc. IEEE/ACM Int. Symp. Low Power Electron. Design, 2014, pp. 15-20.
116. J. Kim et al., "Spin-based computing: Device concepts, current status, and a case study on a high-performance microprocessor," Proc. IEEE, vol. 103, no. 1, pp. 106-130, Jan. 2015.
117. A. P. Malozemoff and J. C. Slonczewski, Magnetic Domain Walls in Bubble Materials. New York, NY, USA: Academic, 1979.
118. N. Nagaosa and Y. Tokura, "Topological properties and dynamics of magnetic skyrmions," Nature Nanotechnol., vol. 8, no. 12, pp. 899-911, Dec. 2013.
119. S. Ladak, D. E. Read, G. K. Perkins, L. F. Cohen, and W. R. Branford, "Direct observation of magnetic monopole defects in an artificial spin-ice system," Nature Phys., vol. 6, no. 5, pp. 359-363, May 2010.
120. A. V. Chumak, V. I. Vasyuchka, A. A. Serga, and B. Hillebrands, "Magnon spintronics," Nature Phys., vol. 11, no. 6, pp. 453-461, Jun. 2015.
121. M. T. Niemier et al., "Nanomagnet logic: Progress toward system-level integration," J. Phys. Condens. Matter, vol. 23, no. 49, 2011, Art. no. 493202.
122. A. H. Eschenfelder, Magnetic Bubble Technology. New York, NY, USA: Springer Science & Business Media, 1980.
123. S. Parkin and S.-H. Yang, "Memory on the racetrack," Nature Nanotechnol., vol. 10, no. 3, pp. 195-198, Mar. 2015.
124. A. Farhan et al., "Exploring hyper-cubic energy landscapes in thermally active finite artificial spin-ice systems," Nature Phys., vol. 9, no. 6, pp. 375-382, Jun. 2013.
125. Y.-L. Wang et al., "Rewritable artificial magnetic charge ice," Science, vol. 352, no. 6288, pp. 962-966, May 2016.
126. W. L. Shew et al., "Adaptation to sensory input tunes visual cortex to criticality," Nature Phys., vol. 11, no. 8, pp. 659-663, Aug. 2015.
127. G. Buzsaki, Rhythms of the Brain, 1st ed. New York, NY, USA: OUP USA, 2011.
128. E. M. Izhikevich, "Which model to use for cortical spiking neurons?" IEEE Trans. Neural Netw., vol. 15, no. 5, pp. 1063-1070, Sep. 2004.
129. J. Fell and N. Axmacher, "The role of phase synchronization in memory processes," Nature Rev. Neurosci., vol. 12, no. 2, pp. 105-118, Feb. 2011.
130. M. Rabinovich, R. Huerta, and G. Laurent, "Transient dynamics for neural processing," Science, vol. 321, no. 5885, pp. 48-50, Jul. 2008.
131. O. Marre, P. Yger, A. P. Davison, and Y. Frégnac, "Reliable recall of spontaneous activity patterns in cortical networks," J. Neurosci., vol. 29, no. 46, pp. 14 596-14 606, Nov. 2009.
132. H. Sompolinsky, A. Crisanti, and H. J. Sommers, "Chaos in random neural networks," Phys. Rev. Lett., vol. 61, no. 3, pp. 259-262, Jul. 1988.
133. C. Eliasmith, "Attractor network," Scholarpedia, vol. 2, no. 10, p. 1380, 2007.
134. S. Bhanja, D. K. Karunaratne, R. Panchumarthy, S. Rajaram, and S. Sarkar, "Non-Boolean computing with nanomagnets for computer vision applications," Nature Nanotechnol., vol. 11, no. 2, pp. 177-183, Feb. 2016.
135. T. Aonishi, "Phase transitions of an oscillator neural network with a standard Hebb learning rule," Phys. Rev. E, vol. 58, no. 4, pp. 4865-4871, Oct. 1998.
136. D. E. Nikonov et al., "Coupled-oscillator associative memory array operation for pattern recognition," IEEE J. Explor. Solid-State Comput. Devices Circuits, vol. 1, pp. 85-93, Dec. 2015.
137. M. R. Pufall et al., "Physical implementation of coherently coupled oscillator networks," IEEE J. Explor. Solid-State Comput. Devices Circuits, vol. 1, pp. 76-84, Dec. 2015.
138. K. Yogendra, D. Fan, and K. Roy, "Coupled spin torque nano oscillators for low power neural computation," IEEE Trans. Magn., vol. 51, no. 10, pp. 1-9, Oct. 2015.
139. W. Rippard, M. Pufall, and A. Kos, "Time required to injection-lock spin torque nanoscale oscillators," Appl. Phys. Lett., vol. 103, no. 18, Oct. 2013, Art. no. 182403.
140. P. Krzysteczko, J. Münchenberger, M. Schäfers, G. Reiss, and A. Thomas, "The memristive magnetic tunnel junction as a nanoscopic synapse-neuron system," Adv. Mater., vol. 24, no. 6, pp. 762-766, Feb. 2012.
141. D. I. Suh, G. Y. Bae, H. S. Oh, and W. Park, "Neural coding using telegraphic switching of magnetic tunnel junction," J. Appl. Phys., vol. 117, no. 17, May 2015, Art. no. 17D714.
142. A. Mizrahi et al., "Controlling the phase locking of stochastic magnetic bits for ultra-low power computation," Sci. Rep., vol. 6, p. 30 535, Jul. 2016.
143. A. Chanthbouala et al., "A ferroelectric memristor," Nature Mater., vol. 11, no. 10, pp. 860-864, Oct. 2012.
144. S. Petit-Watelot et al., "Commensurability and chaos in magnetic vortex oscillations," Nature Phys., vol. 8, no. 9, pp. 682-687, Sep. 2012.
145. M. D. Pickett, G. Medeiros-Ribeiro, and R. S. Williams, "A scalable neuristor built with Mott memristors," Nature Mater., vol. 12, no. 2, pp. 114-117, Feb. 2013.
146. A. Flocke and T. G. Noll, "Fundamental analysis of resistive nano-crossbars for the use in hybrid Nano/CMOS-memory," in Proc. 33rd Eur. Solid State Circuits Conf., 2007, pp. 328-331.
147. K. Chen, "Deep and modular neural networks," in Springer Handbook of Computational Intelligence, J. Kacprzyk and W. Pedrycz, Eds. Berlin, Germany: Springer-Verlag, 2015, pp. 473-494.