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p-bits for probabilistic spin logic 원문보기

Applied physics reviews : APR, v.6 no.1, 2019년, pp.011305 -   

Camsari, Kerem Y. (School of Electrical and Computer Engineering, Purdue University , West Lafayette, Indiana 47907, USA) ,  Sutton, Brian M. (School of Electrical and Computer Engineering, Purdue University , West Lafayette, Indiana 47907, USA) ,  Datta, Supriyo (School of Electrical and Computer Engineering, Purdue University , West Lafayette, Indiana 47907, USA)

Abstract AI-Helper 아이콘AI-Helper

We introduce the concept of a probabilistic or p-bit, intermediate between the standard bits of digital electronics and the emerging q-bits of quantum computing. We show that low barrier magnets or LBMs provide a natural physical representation for p-bits and can be built either from perpendicular m...

참고문헌 (86)

  1. IEEE Trans. Magn. 46 1873 2010 10.1109/TMAG.2010.2042041 Advances and future prospects of spin-transfer torque random access memory 

  2. Phys. Rev. B 65 224406 2002 10.1103/PhysRevB.65.224406 Transition from ferromagnetism to superparamagnetism on the nanosecond time scale 

  3. Phys. Rev. Appl. 2 034009 2014 10.1103/PhysRevApplied.2.034009 Noise-enhanced synchronization of stochastic magnetic oscillators 

  4. AIP Adv. 8 055903 2018 10.1063/1.5006422 Superparamagnetic perpendicular magnetic tunnel junctions for true random number generators 

  5. Phys. Rev. Appl. 8 054045 2017 10.1103/PhysRevApplied.8.054045 Low-energy truly random number generation with superparamagnetic tunnel junctions for unconventional computing 

  6. 1 2018 Circuit-level evaluation of the generation of truly random bits with superparamagnetic tunnel junctions 

  7. IEEE Magn. Lett. 9 4305205 2018 10.1109/LMAG.2018.2860547 Designing stochastic nanomagnets for probabilistic spin logic 

  8. Phys. Rev. Lett. 83 1042 1999 10.1103/PhysRevLett.83.1042 Single-domain circular nanomagnets 

  9. 34.3.1 2016 Experimental demonstration of nanomagnet networks as hardware for Ising computing 

  10. Sci. Rep. 7 44370 2017 10.1038/srep44370 Intrinsic optimization using stochastic nanomagnets 

  11. IEEE Magn. Lett. 8 1 2017 10.1109/LMAG.2017.2685358 Low-barrier nanomagnets as p-bits for spin logic 

  12. Phys. Rev. X 7 031014 2017 10.1103/PhysRevX.7.031014 Stochastic p-bits for invertible logic 

  13. IEEE Electron Device Lett. 38 1767 2017 10.1109/LED.2017.2768321 Implementing p-bits with embedded MTJ 

  14. Nat. Commun. 9 1533 2018 10.1038/s41467-018-03963-w Neural-like computing with populations of superparamagnetic basis functions 

  15. Appl. Phys. Lett. 111 243107 2017 10.1063/1.5012091 Current control of time-averaged magnetization in superparamagnetic tunnel junctions 

  16. Sci. Rep. 6 29893 2016 10.1038/srep29893 A building block for hardware belief networks 

  17. Int. J. Theor. Phys. 21 467 1982 10.1007/BF02650179 Simulating physics with computers 

  18. Cognit. Sci. 9 147 1985 10.1207/s15516709cog0901_7 A learning algorithm for Boltzmann machines 

  19. Artif. Intell. 56 71 1992 10.1016/0004-3702(92)90065-6 Connectionist learning of belief networks 

  20. 19 2016 Dot-product engine for neuromorphic computing: Programming 1t1m crossbar to accelerate matrix-vector multiplication 

  21. Mater. Today 20 530 2017 10.1016/j.mattod.2017.07.007 Spintronics based random access memory: A review 

  22. 36 2012 Modeling circuits with spins and magnets for all-spin logic 

  23. B. Behin-Aein, “Computing multi-magnet based devices and methods for solution of optimization problems,” U.S. patent 8,698,517 (2014). 

  24. 12 2014 A magnetic tunnel junction based true random number generator with conditional perturb and real-time output probability tracking 

  25. Appl. Phys. Express 7 083001 2014 10.7567/APEX.7.083001 Spin dice: A scalable truly random number generator based on spintronics 

  26. IEEE Trans. Biomed. Circuits Syst. 9 166 2015 10.1109/TBCAS.2015.2414423 Spin-transfer torque magnetic memory as a stochastic memristive synapse for neuromorphic systems 

  27. IEEE Trans. Electron Devices 63 2963 2016 10.1109/TED.2016.2568762 Probabilistic deep spiking neural systems enabled by magnetic tunnel junction 

  28. 36 2017 A single magnetic-tunnel-junction stochastic computing unit 

  29. IEEE Trans. Pattern Anal. Mach. Intell. 6 721 1984 10.1109/TPAMI.1984.4767596 Stochastic relaxation, gibbs distributions, and the bayesian restoration of images 

  30. IEEE Trans. Neural Networks Learn. Syst. Weighted p-bits for fpga implementation of probabilistic circuits 

  31. 2005 A probabilistic CMOS switch and its realization by exploiting noise 

  32. IEEE Trans. Comput. 52 403 2003 10.1109/TC.2003.1190581 A high-speed oscillator-based truly random number source for cryptographic applications on a smart card IC 

  33. IEEE Trans. Comput. 58 1198 2009 10.1109/TC.2008.212 Power-up SRAM state as an identifying fingerprint and source of true random numbers 

  34. J. Appl. Phys. 97 10D509 2005 10.1063/1.1857655 Programmable spintronics logic device based on a magnetic tunnel junction element 

  35. Appl. Phys. Express 1 091301 2008 10.1143/APEX.1.091301 Fabrication of a nonvolatile full adder based on logic-in-memory architecture using magnetic tunnel junctions 

  36. 9 2010 Magnetic tunnel junction for nonvolatile cmos logic 

  37. IEEE Trans. Magn. 47 2970 2011 10.1109/TMAG.2011.2158527 Magnetic tunnel junction logic architecture for realization of simultaneous computation and communication 

  38. IEEE Trans. Nanotechnol. 11 120 2012 10.1109/TNANO.2011.2158848 Magnetic tunnel junction-based spintronic logic units operated by spin transfer torque 

  39. Proc. IEEE 104 2024 2016 10.1109/JPROC.2016.2597152 Spintronic nanodevices for bioinspired computing 

  40. Nat. Mater. 13 11 2014 10.1038/nmat3823 Spin-torque building blocks 

  41. Y. Cao, T. Sato, D. Sylvester, M. Orshansky, and C. Hu, “Predictive technology model,” (2002), see http://ptm.asu.edu. 

  42. Phys. Rev. Appl. 8 064017 2017 10.1103/PhysRevApplied.8.064017 Stochastic spiking neural networks enabled by magnetic tunnel junctions: From nontelegraphic to telegraphic switching regimes 

  43. IEEE J. Explor. Solid-State Comput. Devices Circuits 1 3 2015 10.1109/JXCDC.2015.2418033 Benchmarking of beyond-cmos exploratory devices for logic integrated circuits 

  44. Phys. Rev. Appl. 9 044020 2018 10.1103/PhysRevApplied.9.044020 Equivalent circuit for magnetoelectric read and write operations 

  45. Nano Lett. 17 3478 2017 10.1021/acs.nanolett.7b00439 Experimental demonstration of complete 180° reversal of magnetization in isolated co nanomagnets on a pmn-pt substrate with voltage generated strain 

  46. Nat. Phys. 14 338 2018 10.1038/s41567-018-0101-4 Beyond cmos computing with spin and polarization 

  47. 1 2017 A random number generator based on insulator-to-metal electronic phase transitions 

  48. J. ACM 20 456 1973 10.1145/321765.321777 Generalized feedback shift register pseudorandom number algorithm 

  49. IEEE Trans. Electron Devices 39 1444 1992 10.1109/16.137325 A functional MOS transistor featuring gate-level weighted sum and threshold operations 

  50. 10.1109/MDAT.2019.2897964 O. Hassan, K. Y. Camsari, and S. Datta, “Voltage-driven building block for hardware belief networks,” e-print arXiv:1801.09026 [cs] (2018). 

  51. Design of Interconnection Networks for Programmable Logic 2004 

  52. Science 345 668 2014 10.1126/science.1254642 A million spiking-neuron integrated circuit with a scalable communication network and interface 

  53. Sci. Rep. 7 10994 2017 10.1038/s41598-017-11011-8 Hardware emulation of stochastic p-bits for invertible logic 

  54. Sci. Rep. 5 10571 2015 10.1038/srep10571 Modular approach to spintronics 

  55. ACM Trans. Des. Autom. Electron. Syst. (TODAES) 12 29 2007 10.1145/1255456.1255466 Probabilistic system-on-a-chip architectures 

  56. Proc. IEEE 103 1398 2015 10.1109/JPROC.2015.2437616 Bioinspired programming of memory devices for implementing an inference engine 

  57. Sci. Rep. 7 14101 2017 10.1038/s41598-017-14240-z Stochastic spin-orbit torque devices as elements for bayesian inference 

  58. Comput. Biol. Med. 69 245 2016 10.1016/j.compbiomed.2015.08.015 Real-time prediction of acute cardiovascular events using hardware-implemented bayesian networks 

  59. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 25 2688 2017 10.1109/TVLSI.2017.2654298 Vlsi implementation of deep neural network using integral stochastic computing 

  60. 15 2018 Low-energy deep belief networks using intrinsic sigmoidal spintronic-based probabilistic neurons 

  61. 791 2007 Restricted Boltzmann machines for collaborative filtering 

  62. Neural Comput. 14 1771 2002 10.1162/089976602760128018 Training products of experts by minimizing contrastive divergence 

  63. 1 2016 Memristive Boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning 

  64. Accelerating machine learning using stochastic embedded mtj 

  65. Modeling Brain Function: The World of Attractor Neural Networks 1992 

  66. IEEE J. Solid-State Circuits 51 303 2016 10.1109/JSSC.2015.2498601 A 20k-spin ising chip to solve combinatorial optimization problems with cmos annealing 

  67. Science 354 614 2016 10.1126/science.aah5178 A fully programmable 100-spin coherent ising machine with all-to-all connections 

  68. J. Appl. Phys. 121 193902 2017 10.1063/1.4983636 Ising computation based combinatorial optimization using spin-hall effect (she) induced stochastic magnetization reversal 

  69. T. Wang and J. Roychowdhury, “Oscillator-based ising machine,” preprint arXiv:1709.08102 (2017). 

  70. Proc. SPIE 10537 105370D 2018 10.1117/12.2288586 How coherent ising machines push circuit design in silicon photonics to its limits (conference presentation) 

  71. G. E. Hinton, “A practical guide to training restricted Boltzmann machines,” in Neural networks: Tricks of the trade (Springer, 1985); available at https://link.springer.com/chapter/10.1007/978-3-642-35289-8_32. 

  72. Math. Program. 39 117 1987 10.1007/BF02592948 Some np-complete problems in quadratic and nonlinear programming 

  73. SIAM Rev. 41 303 1999 10.1137/S0036144598347011 Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer 

  74. Chaos 27 023107 2017 10.1063/1.4975761 Polynomial-time solution of prime factorization and np-complete problems with digital memcomputing machines 

  75. J. Appl. Phys. 123 180901 2018 10.1063/1.5026506 Perspective: Memcomputing: Leveraging memory and physics to compute efficiently 

  76. Front. Phys. 2 5 2014 10.3389/fphy.2014.00005 Ising formulations of many np problems 

  77. Phys. Rev. Lett. 101 220405 2008 10.1103/PhysRevLett.101.220405 Quantum adiabatic algorithm for factorization and its experimental implementation 

  78. P. Henelius and S. Girvin, “A statistical mechanics approach to the factorization problem,” e-print arXiv:1102.1296 [cond-mat]. 

  79. Sci. Rep. 8 1 2018 10.1038/s41598-018-36058-z Quantum annealing for prime factorization 

  80. Science 220 671 1983 10.1126/science.220.4598.671 Optimization by simulated annealing 

  81. Science 285 1036 1999 10.1126/science.285.5430.1036 Josephson persistent-current qubit 

  82. Nature 473 194 2011 10.1038/nature10012 Quantum annealing with manufactured spins 

  83. Rev. Mod. Phys. 90 015002 2018 10.1103/RevModPhys.90.015002 Adiabatic quantum computation 

  84. K. Y. Camsari, S. Chowdhury, and S. Datta, “Scaled quantum circuits emulated with room temperature p-bits,” preprint arXiv:1810.07144 (2018). 

  85. IEEE Magn. Lett. 7 1 2016 10.1109/LMAG.2016.2610942 Ultrafast spin-transfer-torque switching of synthetic ferrimagnets 

  86. U. Atxitia, T. Birk, S. Selzer, and U. Nowak, “Superparamagnetic limit of antiferromagnetic nanoparticles,” preprint arXiv:1808.07665 (2018). 

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