Physics for neuromorphic computing

Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. We discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time. Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Including more physics in the algorithms and nanoscale materials used for computing could have a major impact in this field.

[1]  Rainer Waser,et al.  Phase-Change and Redox-Based Resistive Switching Memories , 2015, Proceedings of the IEEE.

[2]  Dirk Englund,et al.  Deep learning with coherent nanophotonic circuits , 2017, 2017 Fifth Berkeley Symposium on Energy Efficient Electronic Systems & Steep Transistors Workshop (E3S).

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

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

[5]  Miguel Angel Lastras-Montaño,et al.  Resistive random-access memory based on ratioed memristors , 2018, Nature Electronics.

[6]  Xiaochen Peng,et al.  NeuroSim+: An integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures , 2017, 2017 IEEE International Electron Devices Meeting (IEDM).

[7]  Peng Lin,et al.  Fully memristive neural networks for pattern classification with unsupervised learning , 2018 .

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

[9]  I. K. Yoo,et al.  Ferroelectric materials for neuromorphic computing , 2019, APL Materials.

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

[11]  Big data needs a hardware revolution , 2018, Nature.

[12]  J. Yang,et al.  Robust memristors based on layered two-dimensional materials , 2018, 1801.00530.

[13]  Supriyo Datta,et al.  Integer factorization using stochastic magnetic tunnel junctions , 2019, Nature.

[14]  H. John Caulfield,et al.  Why future supercomputing requires optics , 2010 .

[15]  Matthew Cook,et al.  Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[16]  Laurent Larger,et al.  High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification , 2017 .

[17]  Rodney S. Tucker,et al.  The role of optics in computing , 2010 .

[18]  Dmitri B. Strukov,et al.  Implementation of multilayer perceptron network with highly uniform passive memristive crossbar circuits , 2017, Nature Communications.

[19]  Meng-Fan Chang,et al.  33.2 A Fully Integrated Analog ReRAM Based 78.4TOPS/W Compute-In-Memory Chip with Fully Parallel MAC Computing , 2020, 2020 IEEE International Solid- State Circuits Conference - (ISSCC).

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

[21]  C. Wiegand,et al.  2 MB Array-Level Demonstration of STT-MRAM Process and Performance Towards L4 Cache Applications , 2019, 2019 IEEE International Electron Devices Meeting (IEDM).

[22]  M. Rozenberg,et al.  A Leaky‐Integrate‐and‐Fire Neuron Analog Realized with a Mott Insulator , 2017 .

[23]  多賀 厳太郎,et al.  Dynamical Systems Approach , 2001 .

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

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

[26]  Daniel Brunner,et al.  Fabrication of dense diameter-tuned quantum dot micropillar arrays for applications in photonic information processing , 2018, APL Photonics.

[27]  William J. Dally,et al.  Hardware-Enabled Artificial Intelligence , 2018, 2018 IEEE Symposium on VLSI Circuits.

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

[29]  Yi Luo,et al.  All-optical machine learning using diffractive deep neural networks , 2018, Science.

[30]  Idan Segev,et al.  Single Cortical Neurons as Deep Artificial Neural Networks , 2019 .

[31]  Stefan Schliebs,et al.  Evolving spiking neural network—a survey , 2013, Evolving Systems.

[32]  D. Hassabis,et al.  Neuroscience-Inspired Artificial Intelligence , 2017, Neuron.

[33]  Michael Cross,et al.  Complex Dynamical Networks Constructed with Fully Controllable Nonlinear Nanomechanical Oscillators. , 2017, Nano letters.

[34]  Damien Querlioz,et al.  Bioinspired Programming of Memory Devices for Implementing an Inference Engine , 2015, Proceedings of the IEEE.

[35]  Andrew S. Cassidy,et al.  Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware , 2016, 2016 IEEE International Conference on Rebooting Computing (ICRC).

[36]  S. Laughlin,et al.  An Energy Budget for Signaling in the Grey Matter of the Brain , 2001, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[37]  Fabien Alibart,et al.  Neuromorphic Time‐Dependent Pattern Classification with Organic Electrochemical Transistor Arrays , 2018, Advanced Electronic Materials.

[38]  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.

[39]  D. Querlioz,et al.  Hybrid Analog-Digital Learning with Differential RRAM Synapses , 2019, 2019 IEEE International Electron Devices Meeting (IEDM).

[40]  Sommers,et al.  Chaos in random neural networks. , 1988, Physical review letters.

[41]  R. Waser,et al.  Resistive Switching: From Fundamentals of Nanoionic Redox Processes to Memristive Device Applications , 2016 .

[42]  P. Mattavelli,et al.  Truly Innovative 28nm FDSOI Technology for Automotive Micro-Controller Applications embedding 16MB Phase Change Memory , 2018, 2018 IEEE International Electron Devices Meeting (IEDM).

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

[44]  Damien Querlioz,et al.  In-situ learning harnessing intrinsic resistive memory variability through Markov Chain Monte Carlo Sampling , 2020, ArXiv.

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

[46]  A S Spinelli,et al.  Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity , 2017, Scientific Reports.

[47]  Damien Querlioz,et al.  In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications , 2020, 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE).

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

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

[50]  S. Ambrogio,et al.  Reducing the Impact of Phase-Change Memory Conductance Drift on the Inference of large-scale Hardware Neural Networks , 2019, 2019 IEEE International Electron Devices Meeting (IEDM).

[51]  Geert Morthier,et al.  Experimental demonstration of reservoir computing on a silicon photonics chip , 2014, Nature Communications.

[52]  S. Lockery.  The computational worm: spatial orientation and its neuronal basis in C. elegans , 2011, Current Opinion in Neurobiology.

[53]  Serge Massar,et al.  Brain-Inspired Photonic Signal Processor for Generating Periodic Patterns and Emulating Chaotic Systems , 2017 .

[54]  Wei Yi,et al.  Biological plausibility and stochasticity in scalable VO2 active memristor neurons , 2018, Nature Communications.

[55]  Kwabena Boahen,et al.  A Neuromorph's Prospectus , 2017, Computing in Science & Engineering.

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

[57]  B. Romeira,et al.  Regenerative memory in time-delayed neuromorphic photonic resonators , 2016, Scientific Reports.

[58]  Suman Datta,et al.  Computational paradigms using oscillatory networks based on state-transition devices , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[59]  Yongsung Ji,et al.  Flexible and twistable non-volatile memory cell array with all-organic one diode–one resistor architecture , 2013, Nature Communications.

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

[61]  Damien Querlioz,et al.  Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays , 2019, Frontiers in Neuroscience.

[62]  Julien Sylvestre,et al.  Computing with networks of nonlinear mechanical oscillators , 2017, PloS one.

[63]  Yeshaiahu Fainman,et al.  Metallic coaxial nanolasers , 2016, 1604.06816.

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

[65]  Andrew McCallum,et al.  Energy and Policy Considerations for Deep Learning in NLP , 2019, ACL.

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

[67]  Kwabena Boahen,et al.  Braindrop: A Mixed-Signal Neuromorphic Architecture With a Dynamical Systems-Based Programming Model , 2019, Proceedings of the IEEE.

[68]  Gabriele Navarro,et al.  Toward ultimate nonvolatile resistive memories: The mechanism behind ovonic threshold switching revealed , 2020, Science Advances.

[69]  M. BrightSky,et al.  On-Chip Trainable 1.4M 6T2R PCM Synaptic Array with 1.6K Stochastic LIF Neurons for Spiking RBM , 2019, 2019 IEEE International Electron Devices Meeting (IEDM).

[70]  L Pesquera,et al.  Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. , 2012, Optics express.

[71]  Ryutaro Yasuhara,et al.  A 4M Synapses integrated Analog ReRAM based 66.5 TOPS/W Neural-Network Processor with Cell Current Controlled Writing and Flexible Network Architecture , 2018, 2018 IEEE Symposium on VLSI Technology.

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

[73]  Dharmendra S. Modha,et al.  Backpropagation for Energy-Efficient Neuromorphic Computing , 2015, NIPS.

[74]  M. London,et al.  Dendritic computation. , 2005, Annual review of neuroscience.

[75]  Keisuke Fujii,et al.  Harnessing disordered quantum dynamics for machine learning , 2016, 1602.08159.

[76]  Dean V. Buonomano,et al.  ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS , 2012, Nature Neuroscience.

[77]  Andrew McCallum,et al.  Energy and Policy Considerations for Modern Deep Learning Research , 2020, AAAI.

[78]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[79]  T. Branco,et al.  Local Dendritic Activity Sets Release Probability at Hippocampal Synapses , 2008, Neuron.

[80]  M. Stiles,et al.  Neuromorphic spintronics , 2020, Nature Electronics.

[81]  Yusuf Leblebici,et al.  Neuromorphic computing with multi-memristive synapses , 2017, Nature Communications.

[82]  Olivier Bichler,et al.  Phase change memory as synapse for ultra-dense neuromorphic systems: Application to complex visual pattern extraction , 2011, 2011 International Electron Devices Meeting.

[83]  Jacques-Olivier Klein,et al.  Outstanding Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks , 2019, 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS).

[84]  Armantas Melianas,et al.  Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing , 2019, Science.

[85]  R. French.  Catastrophic forgetting in connectionist networks , 1999, Trends in Cognitive Sciences.

[86]  Michael L. Schneider,et al.  Stochastic single flux quantum neuromorphic computing using magnetically tunable Josephson junctions , 2016, 2016 IEEE International Conference on Rebooting Computing (ICRC).

[87]  D. Querlioz,et al.  Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices , 2013, IEEE Transactions on Nanotechnology.

[88]  J. Feldmann,et al.  All-optical spiking neurosynaptic networks with self-learning capabilities , 2019, Nature.

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

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

[91]  J. Grollier,et al.  A ferroelectric memristor. , 2012, Nature materials.

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

[93]  P. Bai,et al.  Non-Volatile RRAM Embedded into 22FFL FinFET Technology , 2019, 2019 Symposium on VLSI Technology.

[94]  Nicolas Locatelli,et al.  Learning through ferroelectric domain dynamics in solid-state synapses , 2017, Nature Communications.

[95]  Jari A. K. Hyttinen,et al.  A Computational Model of Interactions Between Neuronal and Astrocytic Networks: The Role of Astrocytes in the Stability of the Neuronal Firing Rate , 2020, Frontiers in Computational Neuroscience.

[96]  Rishidev Chaudhuri,et al.  Computational principles of memory , 2016, Nature Neuroscience.

[97]  Sangheon Oh,et al.  A Soft-Pruning Method Applied During Training of Spiking Neural Networks for In-memory Computing Applications , 2019, Front. Neurosci..

[98]  John D. Joannopoulos,et al.  Controlling Directionality and Dimensionality of Radiation by Perturbing Separable Bound States in the Continuum , 2015, Scientific Reports.

[99]  Ali Khiat,et al.  Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses , 2016, Nature Communications.

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

[101]  G. Bi,et al.  Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[102]  Zhisheng Huang,et al.  Adaptive Quantization as a Device-Algorithm Co-Design Approach to Improve the Performance of In-Memory Unsupervised Learning With SNNs , 2019, IEEE Transactions on Electron Devices.

[103]  Wei Pan,et al.  Towards Accurate Binary Convolutional Neural Network , 2017, NIPS.

[104]  Laurent Larger,et al.  Three dimensional waveguide-interconnects for scalable integration of photonic neural networks , 2019, Optica.

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

[106]  Demis Hassabis,et al.  Mastering the game of Go without human knowledge , 2017, Nature.

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

[108]  F. García-Sánchez,et al.  The design and verification of MuMax3 , 2014, 1406.7635.

[109]  Konstantin K. Likharev,et al.  Neuromorphic architectures for nanoelectronic circuits , 2004, Int. J. Circuit Theory Appl..

[110]  Timothée Masquelier,et al.  First-Spike-Based Visual Categorization Using Reward-Modulated STDP , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[111]  D. Chialvo.  Emergent complex neural dynamics , 2010, 1010.2530.

[112]  Mario Miscuglio,et al.  All-optical nonlinear activation function for photonic neural networks [Invited] , 2018, Optical Materials Express.

[113]  Michael L. Schneider,et al.  Ultralow power artificial synapses using nanotextured magnetic Josephson junctions , 2018, Science Advances.

[114]  T. Ghani,et al.  MRAM as Embedded Non-Volatile Memory Solution for 22FFL FinFET Technology , 2018, 2018 IEEE International Electron Devices Meeting (IEDM).

[115]  Robert Elliman,et al.  High-endurance megahertz electrical self-oscillation in Ti/NbO x bilayer structures , 2015 .

[116]  Ran El-Yaniv,et al.  Binarized Neural Networks , 2016, ArXiv.

[117]  Idan Segev,et al.  Single cortical neurons as deep artificial neural networks , 2019, Neuron.

[118]  Joshua B. Tenenbaum,et al.  Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.

[119]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[120]  Damien Querlioz,et al.  Using Memristors for Robust Local Learning of Hardware Restricted Boltzmann Machines , 2019, Scientific Reports.

[121]  Pritish Narayanan,et al.  Toward on-chip acceleration of the backpropagation algorithm using nonvolatile memory , 2017, IBM J. Res. Dev..

[122]  Shimeng Yu,et al.  Neuro-Inspired Computing With Emerging Nonvolatile Memorys , 2018, Proceedings of the IEEE.

[123]  Nikil D. Dutt,et al.  Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule , 2013, Neural Networks.

[124]  Eric J. Sandouk,et al.  Atomic switch networks—nanoarchitectonic design of a complex system for natural computing , 2015, Nanotechnology.

[125]  Kaushik Roy,et al.  ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing , 2019, Front. Neurosci..

[126]  V. Semenov,et al.  RSFQ logic/memory family: a new Josephson-junction technology for sub-terahertz-clock-frequency digital systems , 1991, IEEE Transactions on Applied Superconductivity.

[127]  Mohammed Ismail,et al.  Analog VLSI Implementation of Neural Systems , 2011, The Kluwer International Series in Engineering and Computer Science.

[128]  Surya Ganguli,et al.  Continual Learning Through Synaptic Intelligence , 2017, ICML.

[129]  Ken-ichi Kawarabayashi,et al.  Coherent Ising Machine - Optical Neural Network Operating at the Quantum Limit - , 2018, 2018 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR).

[130]  Chiara Macchiavello,et al.  An artificial neuron implemented on an actual quantum processor , 2018, npj Quantum Information.

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

[132]  Eric Pop,et al.  Electronic synapses made of layered two-dimensional materials , 2018, Nature Electronics.

[133]  Idan Segev,et al.  Ion Channel Stochasticity May Be Critical in Determining the Reliability and Precision of Spike Timing , 1998, Neural Computation.

[134]  Bartlett W. Mel,et al.  Pyramidal Neuron as Two-Layer Neural Network , 2003, Neuron.

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

[136]  M. Larkum,et al.  Dendritic action potentials and computation in human layer 2/3 cortical neurons , 2020, Science.

[137]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[138]  Jian Shi,et al.  Neuromimetic Circuits with Synaptic Devices based on Strongly Correlated Electron Systems , 2014, ArXiv.

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

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

[141]  Razvan Pascanu,et al.  Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.

[142]  Damien Rontani,et al.  Human action recognition with a large-scale brain-inspired photonic computer , 2019, Nat. Mach. Intell..

[143]  James A. Bain,et al.  Phase Coupling and Control of Oxide-Based Oscillators for Neuromorphic Computing , 2015, IEEE Journal on Exploratory Solid-State Computational Devices and Circuits.

[144]  Juan Trastoy,et al.  Subthreshold firing in Mott nanodevices , 2019, Nature.

[145]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

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

[147]  Alessandro Calderoni,et al.  Neuromorphic Learning and Recognition With One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM , 2016, IEEE Transactions on Electron Devices.

[148]  A. Selverston,et al.  Dynamical principles in neuroscience , 2006 .

[149]  Dmitri B. Strukov,et al.  A reconfigurable architecture for hybrid CMOS/Nanodevice circuits , 2006, FPGA '06.

[150]  Mirko Hansen,et al.  Memristive stochastic plasticity enables mimicking of neural synchrony: Memristive circuit emulates an optical illusion , 2017, Science Advances.

[151]  Sungho Kim,et al.  Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. , 2015, Nano letters.

[152]  D. Attwell,et al.  Energy-Efficient Information Transfer by Visual Pathway Synapses , 2015, Current Biology.

[153]  A. Pérez-Villalba.  Rhythms of the Brain, G. Buzsáki. Oxford University Press, Madison Avenue, New York (2006), Price: GB £42.00, p. 448, ISBN: 0-19-530106-4 , 2008 .

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

[155]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[156]  Mark D. McDonnell,et al.  The benefits of noise in neural systems: bridging theory and experiment , 2011, Nature Reviews Neuroscience.

[157]  Sangheon Oh,et al.  Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays , 2018, Nature Communications.

[158]  Karl J. Friston.  The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

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

[160]  Rae Silver,et al.  Orchestrating time: arrangements of the brain circadian clock , 2005, Trends in Neurosciences.