Neuromorphic spintronics

[1]  Ferdinand Peper,et al.  Reservoir computing with dipole-coupled nanomagnets , 2018, Japanese Journal of Applied Physics.

[2]  Supriyo Datta,et al.  Scalable Emulation of Stoquastic Hamiltonians with Room Temperature p-bits , 2019 .

[3]  Gerhard Jakob,et al.  Thermal skyrmion diffusion used in a reshuffler device , 2018, Nature Nanotechnology.

[4]  Th. Rasing,et al.  Supervised learning of an opto-magnetic neural network with ultrashort laser pulses , 2018, Applied Physics Letters.

[5]  Daniele Pinna,et al.  Reservoir Computing with Random Skyrmion Textures , 2018, Physical Review Applied.

[6]  C. Back,et al.  Phase programming in coupled spintronic oscillators. , 2018, 1811.02154.

[7]  Hitoshi Kubota,et al.  Evaluation of memory capacity of spin torque oscillator for recurrent neural networks , 2018, Japanese Journal of Applied Physics.

[8]  Damien Querlioz,et al.  Use of Magnetoresistive Random-Access Memory as Approximate Memory for Training Neural Networks , 2018, 2018 25th IEEE International Conference on Electronics, Circuits and Systems (ICECS).

[9]  J. Åkerman,et al.  Ultra-fast logic devices using artificial “neurons” based on antiferromagnetic pulse generators , 2018, Journal of Applied Physics.

[10]  Damien Querlioz,et al.  Nano-oscillator-based classification with a machine learning-compatible architecture , 2018, Journal of Applied Physics.

[11]  Gunnar Tufte,et al.  Computation in artificial spin ice , 2018, ALIFE.

[12]  T. Rasing,et al.  Towards massively parallelized all-optical magnetic recording , 2018, Journal of Applied Physics.

[13]  Pritish Narayanan,et al.  Equivalent-accuracy accelerated neural-network training using analogue memory , 2018, Nature.

[14]  Damien Querlioz,et al.  Overcoming device unreliability with continuous learning in a population coding based computing system , 2018, Journal of Applied Physics.

[15]  S. Majetich,et al.  Superparamagnetic perpendicular magnetic tunnel junctions for true random number generators , 2018 .

[16]  Jason Cong,et al.  Scaling for edge inference of deep neural networks , 2018 .

[17]  Xing Chen,et al.  A compact skyrmionic leaky-integrate-fire spiking neuron device. , 2018, Nanoscale.

[18]  Johan Akerman,et al.  Ultra-fast artificial neuron: generation of picosecond-duration spikes in a current-driven antiferromagnetic auto-oscillator , 2018, Scientific Reports.

[19]  Big data needs a hardware revolution. , 2018 .

[20]  Damien Querlioz,et al.  Vowel recognition with four coupled spin-torque nano-oscillators , 2017, Nature.

[21]  Jacques Droulez,et al.  Skyrmion Gas Manipulation for Probabilistic Computing , 2017, Physical Review Applied.

[22]  Hitoshi Kubota,et al.  Neural-like computing with populations of superparamagnetic basis functions , 2016, Nature Communications.

[23]  Damien Querlioz,et al.  Neuromorphic computing through time-multiplexing with a spin-torque nano-oscillator , 2017, 2017 IEEE International Electron Devices Meeting (IEDM).

[24]  Renaud B. Jolivet,et al.  Energy use constrains brain information processing , 2017, 2017 IEEE International Electron Devices Meeting (IEDM).

[25]  Supriyo Datta,et al.  Implementing p-bits With Embedded MTJ , 2017, IEEE Electron Device Letters.

[26]  Supriyo Datta,et al.  R-DBN: A Resistive Deep Belief Network Architecture Leveraging the Intrinsic Behavior of Probabilistic Devices , 2017, ArXiv.

[27]  George Bourianoff,et al.  Potential implementation of reservoir computing models based on magnetic skyrmions , 2017, 1709.08911.

[28]  Yan Zhou,et al.  Magnetic skyrmion-based artificial neuron device , 2017, Nanotechnology.

[29]  Joseph S. Friedman,et al.  Low-Energy Truly Random Number Generation with Superparamagnetic Tunnel Junctions for Unconventional Computing , 2017, 1706.05262.

[30]  P. Fischer,et al.  Three-dimensional nanomagnetism , 2017, Nature Communications.

[31]  Catherine D. Schuman,et al.  A Survey of Neuromorphic Computing and Neural Networks in Hardware , 2017, ArXiv.

[32]  Damien Querlioz,et al.  A Nanotechnology-Ready Computing Scheme based on a Weakly Coupled Oscillator Network , 2017, Scientific Reports.

[33]  Hideo Ohno,et al.  Device-size dependence of field-free spin-orbit torque induced magnetization switching in antiferromagnet/ferromagnet structures , 2017 .

[34]  Johan Åkerman,et al.  Long-range mutual synchronization of spin Hall nano-oscillators , 2016, Nature Physics.

[35]  Benjamin Krueger,et al.  Magnetic Skyrmion as a Nonlinear Resistive Element: A Potential Building Block for Reservoir Computing , 2017, 1702.04298.

[36]  R. Wiesendanger,et al.  Impact of the skyrmion spin texture on magnetoresistance , 2017, 1701.09077.

[37]  Damien Querlioz,et al.  Neuromorphic computing with nanoscale spintronic oscillators , 2017, Nature.

[38]  S. Datta,et al.  Low-Barrier Nanomagnets as p-Bits for Spin Logic , 2016, IEEE Magnetics Letters.

[39]  Yan Zhou,et al.  Magnetic skyrmion-based synaptic devices , 2016, Nanotechnology.

[40]  Supriyo Datta,et al.  Intrinsic optimization using stochastic nanomagnets , 2016, Scientific Reports.

[41]  T. Jungwirth,et al.  Imaging Current-Induced Switching of Antiferromagnetic Domains in CuMnAs. , 2016, Physical review letters.

[42]  A Fukushima,et al.  Mutual synchronization of spin torque nano-oscillators through a long-range and tunable electrical coupling scheme , 2016, Nature Communications.

[43]  Yoshihiko Horio,et al.  Analogue spin–orbit torque device for artificial-neural-network-based associative memory operation , 2016 .

[44]  Supriyo Datta,et al.  Experimental demonstration of nanomagnet networks as hardware for Ising computing , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).

[45]  H. Kanaya,et al.  4Gbit density STT-MRAM using perpendicular MTJ realized with compact cell structure , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).

[46]  Brian M. Sutton,et al.  Stochastic p-bits for Invertible Logic , 2016, 1610.00377.

[47]  Damien Querlioz,et al.  Spintronic Nanodevices for Bioinspired Computing , 2016, Proceedings of the IEEE.

[48]  S. Yuasa,et al.  A magnetic synapse: multilevel spin-torque memristor with perpendicular anisotropy , 2016, Scientific Reports.

[49]  Steve Furber,et al.  Large-scale neuromorphic computing systems , 2016, Journal of neural engineering.

[50]  Hitoshi Kubota,et al.  Controlling the phase locking of stochastic magnetic bits for ultra-low power computation , 2016, Scientific Reports.

[51]  H. Ohno,et al.  A spin-orbit torque switching scheme with collinear magnetic easy axis and current configuration. , 2016, Nature nanotechnology.

[52]  Supriyo Datta,et al.  A building block for hardware belief networks , 2016, Scientific Reports.

[53]  Shoji Ikeda,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 , 2016 .

[54]  Ali Farhadi,et al.  XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks , 2016, ECCV.

[55]  J. Åkerman,et al.  Spin-wave-beam driven synchronization of nanocontact spin-torque oscillators. , 2016, Nature nanotechnology.

[56]  Kaushik Roy,et al.  Magnetic Pattern Recognition Using Injection-Locked Spin-Torque Nano-Oscillators , 2016, IEEE Transactions on Electron Devices.

[57]  Sanjukta Bhanja,et al.  Non-Boolean computing with nanomagnets for computer vision applications. , 2016, Nature nanotechnology.

[58]  Kaushik Roy,et al.  Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons , 2015, Scientific Reports.

[59]  H. Ohno,et al.  Magnetization switching by spin-orbit torque in an antiferromagnet-ferromagnet bilayer system. , 2015, Nature materials.

[60]  A. Rushforth,et al.  Electrical switching of an antiferromagnet , 2015, Science.

[61]  Benjamin Krueger,et al.  Observation of room-temperature magnetic skyrmions and their current-driven dynamics in ultrathin metallic ferromagnets. , 2015, Nature materials.

[62]  Manish Kumar.  Large-scale neuromorphic computing systems , 2016 .

[63]  Y. Leblebici,et al.  Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power) , 2015, 2015 IEEE International Electron Devices Meeting (IEDM).

[64]  T. J. Hayward,et al.  Intrinsic Nature of Stochastic Domain Wall Pinning Phenomena in Magnetic Nanowire Devices , 2015, Scientific Reports.

[65]  Wolfgang Porod,et al.  Physical Implementation of Coherently Coupled Oscillator Networks , 2015, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits.

[66]  S. Heinze,et al.  Electrical detection of magnetic skyrmions by tunnelling non-collinear magnetoresistance. , 2015, Nature nanotechnology.

[67]  Vincent Cros,et al.  Efficient Synchronization of Dipolarly Coupled Vortex-Based Spin Transfer Nano-Oscillators , 2015, Scientific Reports.

[68]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[69]  Wei Ning,et al.  Electrical probing of field-driven cascading quantized transitions of skyrmion cluster states in MnSi nanowires , 2015, Nature communications.

[70]  Jacques-Olivier Klein,et al.  Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[71]  Farnood Merrikh-Bayat,et al.  Training and operation of an integrated neuromorphic network based on metal-oxide memristors , 2014, Nature.

[72]  Robert Legenstein,et al.  A compound memristive synapse model for statistical learning through STDP in spiking neural networks , 2014, Front. Neurosci..

[73]  Vincent Gripon,et al.  A Nonvolatile Associative Memory-Based Context-Driven Search Engine Using 90 nm CMOS/MTJ-Hybrid Logic-in-Memory Architecture , 2014, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[74]  Wulfram Gerstner,et al.  Neuronal Dynamics: From Single Neurons To Networks And Models Of Cognition , 2014 .

[75]  Shoji Ikeda,et al.  Properties of magnetic tunnel junctions with a MgO/CoFeB/Ta/CoFeB/MgO recording structure down to junction diameter of 11 nm , 2014 .

[76]  Hitoshi Kubota,et al.  Noise-enhanced synchronization of stochastic magnetic oscillators , 2014, 1405.4360.

[77]  J. S. Lee,et al.  Spin-transfer torque generated by a topological insulator , 2014, Nature.

[78]  V. Cros,et al.  Spin-torque building blocks. , 2014, Nature Materials.

[79]  Y. Tokura,et al.  Topological properties and dynamics of magnetic skyrmions. , 2013, Nature nanotechnology.

[80]  Mohamad Towfik Krounbi,et al.  Basic principles of STT-MRAM cell operation in memory arrays , 2013 .

[81]  J Joshua Yang,et al.  Memristive devices for computing. , 2013, Nature nanotechnology.

[82]  Benjamin Krueger,et al.  Current-Driven Magnetization Dynamics : Analytical Modeling and Numerical Simulation , 2012 .

[83]  K. Roy,et al.  Spin-Based Neuron Model With Domain-Wall Magnets as Synapse , 2012, IEEE Transactions on Nanotechnology.

[84]  D. Ralph,et al.  Spin-Torque Switching with the Giant Spin Hall Effect of Tantalum , 2012, Science.

[85]  L. Appeltant,et al.  Information processing using a single dynamical node as complex system , 2011, Nature communications.

[86]  S. Bandiera,et al.  Perpendicular switching of a single ferromagnetic layer induced by in-plane current injection , 2011, Nature.

[87]  Konstantin K. Likharev,et al.  CrossNets: Neuromorphic Hybrid CMOS/Nanoelectronic Networks , 2011 .

[88]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[89]  M. W. Johnson,et al.  Quantum annealing with manufactured spins , 2011, Nature.

[90]  J. Grollier,et al.  Vertical-current-induced domain-wall motion in MgO-based magnetic tunnel junctions with low current densities , 2011, 1102.2106.

[91]  J. Fell,et al.  The role of phase synchronization in memory processes , 2011, Nature Reviews Neuroscience.

[92]  C. Rettner,et al.  Dynamics of Magnetic Domain Walls Under Their Own Inertia , 2010, Science.

[93]  Jun Yang,et al.  Energy reduction for STT-RAM using early write termination , 2009, 2009 IEEE/ACM International Conference on Computer-Aided Design - Digest of Technical Papers.

[94]  V. Tiberkevich,et al.  Nonlinear Auto-Oscillator Theory of Microwave Generation by Spin-Polarized Current , 2009, IEEE Transactions on Magnetics.

[95]  Hai Helen Li,et al.  Spintronic Memristor Through Spin-Torque-Induced Magnetization Motion , 2009, IEEE Electron Device Letters.

[96]  Dieter Suter,et al.  Quantum adiabatic algorithm for factorization and its experimental implementation. , 2008, Physical review letters.

[97]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[98]  A. Faisal,et al.  Noise in the nervous system , 2008, Nature Reviews Neuroscience.

[99]  Léon Bottou,et al.  The Tradeoffs of Large Scale Learning , 2007, NIPS.

[100]  Luc Thomas,et al.  Dependence of current and field driven depinning of domain walls on their structure and chirality in permalloy nanowires. , 2006, Physical review letters.

[101]  B. N. Engel,et al.  Phase-locking in double-point-contact spin-transfer devices , 2005, Nature.

[102]  J. Katine,et al.  Mutual phase-locking of microwave spin torque nano-oscillators , 2005, Nature.

[103]  D Petit,et al.  Magnetic Domain-Wall Logic , 2005, Science.

[104]  W. Senn,et al.  Convergence of stochastic learning in perceptrons with binary synapses. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[105]  Kelvin E. Jones,et al.  Neuronal variability: noise or part of the signal? , 2005, Nature Reviews Neuroscience.

[106]  Matthias Troyer,et al.  Computational complexity and fundamental limitations to fermionic quantum Monte Carlo simulations , 2004, Physical review letters.

[107]  Boulder,et al.  Large-angle, gigahertz-rate random telegraph switching induced by spin-momentum transfer , 2004, cond-mat/0404109.

[108]  H. Ohno,et al.  Current-induced domain-wall switching in a ferromagnetic semiconductor structure , 2004, Nature.

[109]  S. Nasu,et al.  Real-space observation of current-driven domain wall motion in submicron magnetic wires. , 2003, Physical review letters.

[110]  W. Rippard,et al.  Direct-current induced dynamics in Co90 Fe10/Ni80 Fe20 point contacts. , 2003, Physical review letters.

[111]  D. Ralph,et al.  Microwave oscillations of a nanomagnet driven by a spin-polarized current , 2003, Nature.

[112]  A. Fert,et al.  Switching a spin valve back and forth by current-induced domain wall motion , 2003, cond-mat/0304312.

[113]  J. Wegrowe,et al.  Current-induced two-level fluctuations in pseudo-spin-valve (Co/Cu/Co) nanostructures. , 2003, Physical review letters.

[114]  W. Singer,et al.  Dynamic predictions: Oscillations and synchrony in top–down processing , 2001, Nature Reviews Neuroscience.

[115]  E. Farhi,et al.  A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem , 2001, Science.

[116]  R. Zemel,et al.  Information processing with population codes , 2000, Nature Reviews Neuroscience.

[117]  E. Izhikevich,et al.  Oscillatory Neurocomputers with Dynamic Connectivity , 1999 .

[118]  Albert Fert,et al.  Giant magnetoresistance in magnetic multilayered nanowires , 1994 .

[119]  Geoffrey E. Hinton,et al.  A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..

[120]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[121]  L. Chua.  Memristor-The missing circuit element , 1971 .

[122]  L. Pinneo.  On noise in the nervous system. , 1966, Psychological review.