Neuromorphic Computing with Memristor Crossbar

[1]  Zhen Li,et al.  A survey of neural network accelerators , 2016, Frontiers of Computer Science.

[2]  Mirko Hansen,et al.  A memristive spiking neuron with firing rate coding , 2015, Front. Neurosci..

[3]  Paul E. Hasler,et al.  Floating Gate Synapses With Spike-Time-Dependent Plasticity , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[4]  W. Regehr,et al.  Short-term synaptic plasticity. , 2002, Annual review of physiology.

[5]  Barry P Rand,et al.  Extremely Low Operating Current Resistive Memory Based on Exfoliated 2D Perovskite Single Crystals for Neuromorphic Computing. , 2017, ACS nano.

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

[7]  Hava T. Siegelmann,et al.  On the Computational Power of Neural Nets , 1995, J. Comput. Syst. Sci..

[8]  Steve B. Furber,et al.  The SpiNNaker Project , 2014, Proceedings of the IEEE.

[9]  S. Ha,et al.  Adaptive oxide electronics: A review , 2011 .

[10]  Eby G. Friedman,et al.  Memristor-Based Circuit Design for Multilayer Neural Networks , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

[11]  Vinod Kumar Joshi,et al.  Spintronics: A contemporary review of emerging electronics devices , 2016 .

[12]  Young Sun,et al.  A Synaptic Transistor based on Quasi‐2D Molybdenum Oxide , 2017, Advanced materials.

[13]  Mohammed A. Zidan,et al.  Temporal Learning Using Second-Order Memristors , 2017, IEEE Transactions on Nanotechnology.

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

[15]  Su‐Ting Han,et al.  Emerging perovskite materials for high density data storage and artificial synapses , 2018 .

[16]  Anthony N. Burkitt,et al.  A Review of the Integrate-and-fire Neuron Model: I. Homogeneous Synaptic Input , 2006, Biological Cybernetics.

[17]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[18]  Paul Wallich.  Tools & Toys - Playing With Cyc , 2008, IEEE Spectrum.

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

[20]  Miao Hu,et al.  ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).

[21]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[22]  Kevin Waugh,et al.  DeepStack: Expert-level artificial intelligence in heads-up no-limit poker , 2017, Science.

[23]  C. Teuscher,et al.  Modeling and Experimental Demonstration of a Hopfield Network Analog-to-Digital Converter with Hybrid CMOS/Memristor Circuits , 2015, Front. Neurosci..

[24]  D. Jeong,et al.  Comprehensive Writing Margin Analysis and its Application to Stacked one Diode‐One Memory Device for High‐Density Crossbar Resistance Switching Random Access Memory , 2016 .

[25]  J. Joshua Yang,et al.  Synaptic electronics and neuromorphic computing , 2016, Science China Information Sciences.

[26]  Elisa Vianello,et al.  Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting , 2016, Front. Neurosci..

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

[28]  Yu Wang,et al.  All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron , 2016, IEEE Transactions on Biomedical Circuits and Systems.

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

[30]  Jiantao Zhou,et al.  Stochastic Memristive Devices for Computing and Neuromorphic Applications , 2013, Nanoscale.

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

[32]  Karthikeyan Sankaralingam,et al.  Dark Silicon and the End of Multicore Scaling , 2012, IEEE Micro.

[33]  Matthew D. Pickett,et al.  Two‐ and Three‐Terminal Resistive Switches: Nanometer‐Scale Memristors and Memistors , 2011 .

[34]  Lei Wang,et al.  Overview of emerging memristor families from resistive memristor to spintronic memristor , 2015, Journal of Materials Science: Materials in Electronics.

[35]  Dominique Vuillaume,et al.  Filamentary switching: synaptic plasticity through device volatility. , 2015, ACS nano.

[36]  J. Yang,et al.  Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. , 2017, Nature materials.

[37]  Wei Lu,et al.  Temporal information encoding in dynamic memristive devices , 2015 .

[38]  C. Gamrat,et al.  Nanotube devices based crossbar architecture: toward neuromorphic computing , 2010, Nanotechnology.

[39]  Bing J. Sheu,et al.  Analog floating-gate synapses for general-purpose VLSI neural computation , 1991 .

[40]  Leon O. Chua.  Resistance switching memories are memristors , 2011 .

[41]  Shimeng Yu,et al.  NbOx based oscillation neuron for neuromorphic computing , 2017 .

[42]  L. F. Abbott,et al.  Building functional networks of spiking model neurons , 2016, Nature Neuroscience.

[43]  R. Waser,et al.  Nanoscale cation motion in TaO(x), HfO(x) and TiO(x) memristive systems. , 2016, Nature nanotechnology.

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

[45]  Özgür Türel,et al.  CrossNets: possible neuromorphic networks based on nanoscale components , 2003, Int. J. Circuit Theory Appl..

[46]  Sander M. Bohte,et al.  Computing with Spiking Neuron Networks , 2012, Handbook of Natural Computing.

[47]  Bertha Guijarro-Berdiñas,et al.  A survey of methods for distributed machine learning , 2012, Progress in Artificial Intelligence.

[48]  B. Rajendran,et al.  Neuromorphic Computing Based on Emerging Memory Technologies , 2016, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[49]  Steven J. Plimpton,et al.  A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications , 2017, BICA 2017.

[50]  Arindam Basu,et al.  On the Non-STDP Behavior and Its Remedy in a Floating-Gate Synapse , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[51]  C. Lohmann,et al.  The developmental stages of synaptic plasticity , 2014, The Journal of physiology.

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

[53]  Kaoru Ota,et al.  Deep Learning for Mobile Multimedia , 2017, ACM Trans. Multim. Comput. Commun. Appl..

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

[55]  Ming Zhang,et al.  Darwin: A neuromorphic hardware co-processor based on spiking neural networks , 2017, J. Syst. Archit..

[56]  Yu Wang,et al.  A Compact Memristor-Based Dynamic Synapse for Spiking Neural Networks , 2017, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[57]  Byoungil Lee,et al.  Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.

[58]  Daniele Ielmini,et al.  Resistive switching memories based on metal oxides: mechanisms, reliability and scaling , 2016 .

[59]  Youngjune Park,et al.  Artificial Synapses with Short- and Long-Term Memory for Spiking Neural Networks Based on Renewable Materials. , 2017, ACS nano.

[60]  Chao Du,et al.  Emulation of synaptic metaplasticity in memristors. , 2017, Nanoscale.

[61]  Rafael Marcos Luque Baena,et al.  Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[62]  Sungho Kim,et al.  Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol. , 2017, ACS nano.

[63]  Seung Hwan Lee,et al.  Reservoir computing using dynamic memristors for temporal information processing , 2017, Nature Communications.

[64]  Yuriy V. Pershin,et al.  Memory effects in complex materials and nanoscale systems , 2010, 1011.3053.

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

[66]  Kwabena Boahen,et al.  Silicon Neurons That Compute , 2012, ICANN.

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

[68]  Wei Lu,et al.  The future of electronics based on memristive systems , 2018, Nature Electronics.

[69]  J. Yang,et al.  Memristive switching mechanism for metal/oxide/metal nanodevices. , 2008, Nature nanotechnology.

[70]  J. Yang,et al.  Three-dimensional crossbar arrays of self-rectifying Si/SiO2/Si memristors , 2017, Nature Communications.

[71]  Jiaming Zhang,et al.  Analogue signal and image processing with large memristor crossbars , 2017, Nature Electronics.

[72]  F. Zeng,et al.  Simulation of synaptic short-term plasticity using Ba(CF3SO3)2-doped polyethylene oxide electrolyte film , 2016, Scientific Reports.

[73]  R. Williams,et al.  Sub-nanosecond switching of a tantalum oxide memristor , 2011, Nanotechnology.

[74]  M. Pickett,et al.  A scalable neuristor built with Mott memristors. , 2013, Nature materials.

[75]  Mustafa Altun,et al.  Permanent and Transient Fault Tolerance for Reconfigurable Nano-Crossbar Arrays , 2017, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[76]  Emmanuelle J. Merced-Grafals,et al.  Repeatable, accurate, and high speed multi-level programming of memristor 1T1R arrays for power efficient analog computing applications , 2016, Nanotechnology.

[77]  Manuel Le Gallo,et al.  Stochastic phase-change neurons. , 2016, Nature nanotechnology.

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

[79]  Wei Zhang,et al.  Effect of Different Binders on the Electrochemical Performance of Metal Oxide Anode for Lithium-Ion Batteries , 2017, Nanoscale Research Letters.

[80]  Kwabena Boahen,et al.  Silicon-Neuron Design: A Dynamical Systems Approach , 2011, IEEE Transactions on Circuits and Systems I: Regular Papers.

[81]  Hangbing Lv,et al.  An Artificial Neuron Based on a Threshold Switching Memristor , 2018, IEEE Electron Device Letters.

[82]  P. Vontobel,et al.  Writing to and reading from a nano-scale crossbar memory based on memristors , 2009, Nanotechnology.

[83]  R. Waser,et al.  Nanoionics-based resistive switching memories. , 2007, Nature materials.

[84]  T. Hasegawa,et al.  Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. , 2011, Nature materials.

[85]  H.-S. Philip Wong,et al.  Face classification using electronic synapses , 2017, Nature Communications.

[86]  Shimeng Yu,et al.  Read/write schemes analysis for novel complementary resistive switches in passive crossbar memory arrays. , 2010, Nanotechnology.

[87]  Luigi Fortuna,et al.  Memristor Crossbar for Adaptive Synchronization , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

[88]  Sally A. McKee,et al.  Hitting the memory wall: implications of the obvious , 1995, CARN.

[89]  R. Williams,et al.  How We Found The Missing Memristor , 2008, IEEE Spectrum.

[90]  John Paul Strachan,et al.  Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing , 2017, Nature.

[91]  Fabien Alibart,et al.  Plasticity in memristive devices for spiking neural networks , 2015, Front. Neurosci..

[92]  Kyeong-Sik Min,et al.  New Memristor-Based Crossbar Array Architecture with 50-% Area Reduction and 48-% Power Saving for Matrix-Vector Multiplication of Analog Neuromorphic Computing , 2014 .

[93]  Jianhui Zhao,et al.  Memristor with Ag‐Cluster‐Doped TiO2 Films as Artificial Synapse for Neuroinspired Computing , 2018 .

[94]  Leon O. Chua,et al.  Everything You Wish to Know About Memristors but Are Afraid to Ask , 2015, Handbook of Memristor Networks.

[95]  Dmitri B Strukov,et al.  Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability , 2017, Nature Communications.

[96]  C. Wright,et al.  Beyond von‐Neumann Computing with Nanoscale Phase‐Change Memory Devices , 2013 .

[97]  Giacomo Indiveri,et al.  Integration of nanoscale memristor synapses in neuromorphic computing architectures , 2013, Nanotechnology.

[98]  Yusuf Leblebici,et al.  Review of advances in neural networks: Neural design technology stack , 2016, Neurocomputing.

[99]  Yuhong Kang,et al.  Observation of Quantized and Partial Quantized Conductance in Polymer-Suspended Graphene Nanoplatelets , 2016, Nanoscale Research Letters.

[100]  Leon O. Chua,et al.  Three Fingerprints of Memristor , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[101]  Kaushik Roy,et al.  Spin Neurons: A Possible Path to Energy-Efficient Neuromorphic Computers , 2013, ArXiv.

[102]  Gökmen Tayfun,et al.  Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations , 2016, Front. Neurosci..

[103]  Zhongyuan Yu,et al.  Infrared Plasmonic Refractive Index Sensor with Ultra-High Figure of Merit Based on the Optimized All-Metal Grating , 2017, Nanoscale Research Letters.

[104]  Hangbing Lv,et al.  Electronic imitation of behavioral and psychological synaptic activities using TiOx/Al2O3-based memristor devices. , 2017, Nanoscale.

[105]  Qing Wan,et al.  Artificial synapse network on inorganic proton conductor for neuromorphic systems. , 2014, Nature communications.

[106]  A. Thomas,et al.  The Memristive Magnetic Tunnel Junction as a Nanoscopic Synapse‐Neuron System , 2012, Advanced materials.

[107]  Henry Markram,et al.  On the computational power of circuits of spiking neurons , 2004, J. Comput. Syst. Sci..

[108]  Myungsoo Kim,et al.  Atomristor: Nonvolatile Resistance Switching in Atomic Sheets of Transition Metal Dichalcogenides. , 2018, Nano letters.

[109]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[110]  Yuchao Yang,et al.  Building Neuromorphic Circuits with Memristive Devices , 2013, IEEE Circuits and Systems Magazine.

[111]  H. Li,et al.  A learnable parallel processing architecture towards unity of memory and computing , 2015, Scientific Reports.

[112]  Ali Khiat,et al.  Emulating short-term synaptic dynamics with memristive devices , 2015, Scientific Reports.

[113]  Gregory S. Snider,et al.  A Defect-Tolerant Computer Architecture: Opportunities for Nanotechnology , 1998 .

[114]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[115]  Ana B. Porto-Pazos,et al.  Parallel Computing for Brain Simulation. , 2017, Current topics in medicinal chemistry.

[116]  Shimeng Yu,et al.  Synaptic electronics: materials, devices and applications , 2013, Nanotechnology.

[117]  P. B. Pillai,et al.  Nanoionics-Based Three-Terminal Synaptic Device Using Zinc Oxide , 2017 .

[118]  Vahid Rashtchi,et al.  Memristor-based approximate matrix multiplier , 2017 .

[119]  Deepak Khosla,et al.  Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition , 2014, International Journal of Computer Vision.

[120]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[121]  Nikil D. Dutt,et al.  Large-Scale Spiking Neural Networks using Neuromorphic Hardware Compatible Models , 2015, ACM J. Emerg. Technol. Comput. Syst..

[122]  F. Zeng,et al.  Recent progress in resistive random access memories: Materials, switching mechanisms, and performance , 2014 .

[123]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

[124]  Ninghui Sun,et al.  DianNao family , 2016, Commun. ACM.

[125]  Hangbing Lv,et al.  Emulating Short-Term and Long-Term Plasticity of Bio-Synapse Based on Cu/a-Si/Pt Memristor , 2017, IEEE Electron Device Letters.

[126]  Qing Wu,et al.  A novel true random number generator based on a stochastic diffusive memristor , 2017, Nature Communications.

[127]  Andrew S. Cassidy,et al.  Convolutional networks for fast, energy-efficient neuromorphic computing , 2016, Proceedings of the National Academy of Sciences.

[128]  M. Marinella,et al.  A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. , 2017, Nature materials.

[129]  Massimiliano Di Ventra,et al.  Experimental demonstration of associative memory with memristive neural networks , 2009, Neural Networks.

[130]  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.

[131]  Robert A. Nawrocki,et al.  A Mini Review of Neuromorphic Architectures and Implementations , 2016, IEEE Transactions on Electron Devices.

[132]  Joseph E LeDoux,et al.  Structural plasticity and memory , 2004, Nature Reviews Neuroscience.

[133]  Wei Lu,et al.  Short-term Memory to Long-term Memory Transition in a Nanoscale Memristor , 2022 .

[134]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

[135]  Kaushik Roy,et al.  STT-SNN: A Spin-Transfer-Torque Based Soft-Limiting Non-Linear Neuron for Low-Power Artificial Neural Networks , 2014, IEEE Transactions on Nanotechnology.

[136]  Y. Pershin,et al.  Spin Memristive Systems: Spin Memory Effects in Semiconductor Spintronics , 2008, 0806.2151.

[137]  R. Dittmann,et al.  Redox‐Based Resistive Switching Memories – Nanoionic Mechanisms, Prospects, and Challenges , 2009, Advanced materials.

[138]  Massimiliano Di Ventra,et al.  Phase-transition driven memristive system , 2009, 0901.0899.

[139]  Damien Querlioz,et al.  Interplay of multiple synaptic plasticity features in filamentary memristive devices for neuromorphic computing , 2016, Scientific Reports.

[140]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.

[141]  Avinoam Kolodny,et al.  Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[142]  Haralampos Pozidis,et al.  Recent Progress in Phase-Change Memory Technology , 2016, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[143]  Chao Du,et al.  Feature Extraction Using Memristor Networks , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[144]  L. Chua.  Memristor, Hodgkin–Huxley, and Edge of Chaos , 2013, Nanotechnology.

[145]  Sergio Gomez Colmenarejo,et al.  Hybrid computing using a neural network with dynamic external memory , 2016, Nature.

[146]  Sapan Agarwal,et al.  Li‐Ion Synaptic Transistor for Low Power Analog Computing , 2017, Advanced materials.

[147]  Rodrigo Alvarez-Icaza,et al.  Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.

[148]  Feng Miao,et al.  Quantized conductance coincides with state instability and excess noise in tantalum oxide memristors , 2016, Nature Communications.

[149]  N. Christoforou,et al.  State of the art of metal oxide memristor devices , 2016 .

[150]  Eric Pop,et al.  Phase change materials and phase change memory , 2014 .

[151]  Catherine E. Graves,et al.  Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine , 2018, Advanced materials.

[152]  Jianning Ding,et al.  Excitatory Post-Synaptic Potential Mimicked in Indium-Zinc-Oxide Synaptic Transistors Gated by Methyl Cellulose Solid Electrolyte , 2016, Scientific Reports.

[153]  R. Waser,et al.  Coexistence of Grain‐Boundaries‐Assisted Bipolar and Threshold Resistive Switching in Multilayer Hexagonal Boron Nitride , 2017 .

[154]  Farnood Merrikh-Bayat,et al.  3-D Memristor Crossbars for Analog and Neuromorphic Computing Applications , 2017, IEEE Transactions on Electron Devices.

[155]  Enrico Prati,et al.  Atomic scale nanoelectronics for quantum neuromorphic devices: comparing different materials , 2016, ArXiv.

[156]  Jayadeva,et al.  Recent trends in neuromorphic engineering , 2016 .

[157]  Zhengguo Xiao,et al.  Energy‐Efficient Hybrid Perovskite Memristors and Synaptic Devices , 2016 .

[158]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[159]  Shimeng Yu,et al.  Fully parallel write/read in resistive synaptic array for accelerating on-chip learning , 2015, Nanotechnology.

[160]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[161]  E. Eleftheriou,et al.  All-memristive neuromorphic computing with level-tuned neurons , 2016, Nanotechnology.

[162]  N. Mohapatra,et al.  Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET , 2017, Scientific Reports.

[163]  Yuchao Yang,et al.  Tuning analog resistive switching and plasticity in bilayer transition metal oxide based memristive synapses , 2017 .

[164]  Hermann Kohlstedt,et al.  Mimic synaptic behavior with a single floating gate transistor: A MemFlash synapse , 2013 .

[165]  Byung-Gook Park,et al.  3-D Floating-Gate Synapse Array With Spike-Time-Dependent Plasticity , 2018, IEEE Transactions on Electron Devices.

[166]  Wei D. Lu,et al.  Experimental Demonstration of Feature Extraction and Dimensionality Reduction Using Memristor Networks. , 2017, Nano letters.