Synaptic electronics and neuromorphic computing
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Turing A. On computable numbers, with an application to the entscheidungs problem. Proc London Math Soc, 1936, 42: 230–265
Turing A. Intelligent machinery. In: Copeland B J, ed. The Essential Turing: Seminal Writings in Computing, Logic, Philosophy, Artificial Intelligence, and Artificial Life plus The Secrets of Enigma. New York: Oxford University Press, 2004. 406–443
Squire L R, Berg D, Bloom F, et al. Fundamental neuroscience. Curr Opin Neurobiol, 2008, 10: 649–654
Kandel E R, Schwartz J H, Jessell T M. Principles of Neural Science. 4th ed. New York: McGraw-Hill Medical, 2000
Bennett M V L, Zukin R S. Electrical coupling and neuronal synchronization in the mammalian brain. Neuron, 2004, 41: 495–511
Zamarreno-Ramos C, Camunas-Mesa L A, Pérez-Carrasco J A, et al. On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex. Front Neurosci, 2011, 5: 1–22
Pereda A E. Electrical synapses and their functional interactions with chemical synapses. Nat Rev Neurosci, 2014, 15: 250–63
Noback C R, Ruggiero D A, Demarest R J, The Human Nervous System: Structure and Function, 6th ed. Totowa: Humana Press, 2005
Versace M, Chandler B. MoNETA: a mind made from memristors. IEEE Spectr, 2010. http://spectrum.ieee.org/robotics/artificial-intelligence/moneta-a-mind-made-from-memristors
Chua L, Adamatzky A. Memristor Networks. Switzerland: Springer International Publishing, 2013
Diorio C, Hasler P, Minch B A, et al. Single-transistor silicon synapse. IEEE Trans Electron Dev, 1996, 43: 1972–1980
Snider G. Amerson R, Carter D, et al. From synapses to circuitry: using memristive memory to explore the electronic brain. Computer, 2011, 44: 21–28
Hylton T. DARPA SyNAPSE Project. Arlington, 2009
Ananthanarayanan R, Esser S K, Simon H D, et al. The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses. In: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, Portland, 2009. 1–12
Merolla P A, Arthur J V, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345: 668–673
Furber S B, Lester D R, Plana L A, et al. Overview of the SpiNNaker system architecture. IEEE Trans Comput, 2013, 62: 2454–2467
Schemmel J, Grubl A, Hartmann S, et al. Live demonstration: a scaled-down version of the BrainScaleS wafer-scale neuromorphic system. In: Proceedings of 2012 IEEE International Symposium on Circuits and Systems, Seoul, 2012. 702
Boahen K. Neurogrid: Emulating a Million Neurons in the Cortex. In: Proceedings of 28th IEEE Engineering in Medicine and Biology Society Annual International Conference, New York, 2006. Supp: 6702
Benjamin B V, Gao P, McQuinn E, et al. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc IEEE, 2014, 102: 699–716
Hebb D O. The first stage of perception: growth of the assembly. In: The Organization of Behavior. Hoboken: John Wiley & Sons Inc., 1949. 60–78
Markram H, Gerstner W, Sjüstrüm P J. A history of spike-timing-dependent plasticity. Front Synaptic Neurosci, 2011, 3: 1–24
Markram H, Lübke J, Frotscher M, et al. Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science, 1997, 275: 213–215
Levy W B, Steward O. Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience, 1983, 8: 791–797
Cooper L N, Bear M F. The BCM theory of synapse modification at 30: interaction of theory with experiment. Nat Rev Neurosci, 2012, 13: 798–810
Bi G Q, Poo M M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci, 1998, 18: 10464–10472
Bienenstock E L, Cooper L N, Munro P W. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J Neurosci, 1982, 2: 32–48
Sejnowski T, Chattarji S, Sfanton P. Induction of synaptic plasticity by hebbian covariance in the hippocampus. In: The Computing Neuron. Boston: Addison-Wesley Longman Publishing Co., 1989. 105–124
Bliss T V P, Lomo T. Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J Physiol, 1973, 232: 331–356
Mulkey R, Herron C, Malenka R. An essential role for protein phosphatases in hippocampal long-term depression. Science, 1993, 261: 1051–1055
Gütig R, Aharonov R, Rotter S, et al. Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity. J Neurosci, 2003, 23: 3697–3714
Rubin J, Lee D D, Sompolinsky H. Equilibrium properties of temporally asymmetric Hebbian plasticity. Physl Rev Lett, 2001, 86: 364–367
van Rossum M C, Bi G Q, Turrigiano G G. Stable Hebbian learning from spike timing-dependent plasticity. J Neurosci, 2000, 20: 8812–8821
Purves D, Augustine G J, Fitzpatrick D, et al. Neuroscience. 2nd ed. Sunderland: Sinauer Associates, 2001
Lee M-J, Lee C B, Lee D, et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5-x /TaO2-x bilayer structures. Nat Mater, 2011, 10: 625–630
Chanthbouala A, Garcia V, Cherifi R O, et al. A ferroelectric memristor. Nat Mater, 2012, 11: 860–864
Yang J J, Pickett M D, Li X, et al. Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotechnol, 2008, 3: 429–433
Wuttig M, Yamada N. Phase-change materials for rewriteable data storage. Nat Mater, 2007, 6: 824–832
Kuzum D, Jeyasingh R G D, Lee B, et al. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. Nano Lett, 2012, 12: 2179–2186
Yang J J, Strukov D B, Stewart D R. Memristive devices for computing. Nat Nanotechnol, 2013, 8: 13–24
Hickmott T W, Hiatt W R. Bistable switching in Niobium oxide diodes. Appl Phys Lett, 1965, 6: 106–108
Hickmott T W. Low-frequency negative resistance in thin anodic oxide films. J Appl Phys, 1962, 33: 2669
Chua L. Resistance switching memories are memristors. Appl Phys A-Mater Sci Process, 2011, 102: 765–783
Rajendran B, Liu Y, Seo J S, et al. Specifications of nanoscale devices and circuits for neuromorphic computational systems. IEEE Trans Electron Dev, 2013, 60: 246–253
Snider G S. Spike-timing-dependent learning in memristive nanodevices. In: Proceedings of 2008 IEEE/ACM International Symposium on Nanoscale Architectures NANOARCH 2008, Anaheim, 2008. 85–92
Yang J J, Miao F, Pickett M D, et al. The mechanism of electroforming of metal oxide memristive switches. Nanotechnology, 2009, 20: 215201
Yang Y, Gao P, Li L, et al. Electrochemical dynamics of nanoscale metallic inclusions in dielectrics. Nat Commun, 2014, 5: 4232
Sarkar B, Lee B, Misra V. Understanding the gradual reset in Pt/Al2O3/Ni RRAM for synaptic applications. Semicond Sci Technol, 2015, 30: 105014
Rolandi M, Josberger E E, Deng Y X. Two-terminal proton conducting devices with synaptic behavior and memory. In: Proceedings of 72nd Device Research Conference, Santa Barbara, 2014. 245–246
Yang R, Terabe K, Yao Y, et al. Synaptic plasticity and memory functions achieved in a WO3-x -based nanoionics device by using the principle of atomic switch operation. Nanotechnology, 2013, 24: 384003
Jung J-W, Park S, Jeong Y-H. ReRAM-based synaptic device for neuromorphic computing. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne VIC, 2014. 1054–1057
Mandal S, El-Amin A, Alexander K, et al. Novel synaptic memory device for neuromorphic computing. Sci Rep, 2014, 4: 5333
Gao B, Liu L, Kang J. Investigation of the synaptic device based on the resistive switching behavior in hafnium oxide. Prog Nat Sci Mater Int, 2015, 25: 47–50
Wang Y-F, Lin Y-C, Wang I-T, et al. Characterization and modeling of nonfilamentary Ta/TaOx/TiO2/Ti analog synaptic device. Sci Rep, 2015, 5: 10150
Yu S M, Wu Y, Jeyasingh R, et al. An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation. IEEE Trans Electron Dev, 2011, 58: 2729–2737
Gao B, Bi Y, Chen H Y, et al. Ultra-low-energy three-dimensional oxide-based electronic synapses for implementation of robust high-accuracy neuromorphic computation systems. ACS Nano, 2014, 8: 6998–7004
Choi H, Jung H, Lee J, et al. An electrically modifiable synapse array of resistive switching memory. Nanotechnology, 2009, 20: 345201
Panwar N, Kumar D, Upadhyay N K, et al. Memristive synaptic plasticity in Pr0.7Ca0.3MnO3 RRAM by bio-mimetic programming. In: Proceedings of 72nd Device Research Conference, Santa Barbara, 2014. 135–136
Pershin Y V, Di Ventra M. Neuromorphic, digital, and quantum computation with memory circuit elements. Proc IEEE, 2012, 100: 2071–2080
Kim S, Du C, Sheridan P, et al. Experimental demonstration of a second-order memristor and its ability to biorealistically implement synaptic plasticity. Nano Lett, 2015, 15: 2203–2211
Valov I, Waser R, Jameson J R, et al. Electrochemical metallization memories—fundamentals, applications, prospects. Nanotechnology, 2011, 22: 254003
Kozicki M N, Gopalan C, Balakrishnan M, et al. Nonvolatile memory based on solid electrolytes. In: Proceedings of Symposium on Non-Volatile Memory Technology, Orlando, 2004. 10–17
Kund M, Beitel G, Pinnow C-U, et al. Conductive bridging RAM (CBRAM): an emerging non-volatile memory technology scalable to sub 20nm. In: Technical Digest of IEEE International Electron Devices Meeting, Washington DC, 2005. 754–757
Hirose Y, Hirose H. Polarity-dependent memory switching and behavior of Ag dendrite in Ag-photodoped amorphous As2S3 films. J Appl Phys, 1976, 47: 2767–2772
Gopalan C, Ma Y, Gallo T, et al. Demonstration of conductive bridging random access memory (CBRAM) in logic CMOS process. In: Proceedings of 2010 IEEE International Memory Workshop, Seoul, 2010. 1–4
Lu W, Jeong D S, Kozicki M, et al. Electrochemical metallization cellsblending nanoionics into nanoelectronics? MRS Bull, 2012, 37: 124–130
Liu Q, Sun J, Lv H, et al. Resistive switching: real-time observation on dynamic growth/dissolution of conductive filaments in oxide-electrolyte-based ReRAM. Adv Mater, 2012, 24: 1774
Ohno T, Hasegawa T, Tsuruoka T, et al. Short-term plasticity and long-term potentiation mimicked in single inorganic synapses. Nat Mater, 2011, 10: 591–595
Atkinson R, Shiffrin R. Human Memory: a Proposed System and its Control Processes. 2nd ed. Psych Learn Motiv, 1968, 2: 89–195
Yu S M, Wong H S P. Modeling the switching dynamics of programmable-metallization-cell (PMC) memory and its application as synapse device for a neuromorphic computation system. In: Proceedings of 2010 IEEE International Electron Devices Meeting (IEDM), San Francisco, 2010. 520–523
Yu S M, Wong H S P. compact modeling of conducting-bridge random-access memory (CBRAM). IEEE Trans Electron Dev, 2011, 58: 1352–1360
Suri M, Querlioz D, Bichler O, et al. Bio-inspired stochastic computing using binary CBRAM synapses. IEEE Trans Electron Dev, 2013, 60: 2402–2409
Mahalanabis D, Barnaby H J, Gonzalez-Velo Y, et al. Incremental resistance programming of programmable metallization cells for use as electronic synapses. Solid State Electron, 2014, 100: 39–44
Jo S H, Chang T, Ebong I, et al. Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett, 2010, 10: 1297–1301
Kim K H, Gaba S, Wheeler D, et al. A functional hybrid memristor crossbar-array/CMOS system for data storage and neuromorphic applications. Nano Lett, 2012, 12: 389–395
Petersen C C, Malenka R C, Nicoll R A, et al. All-or-none potentiation of CA3-CA1 synapses. Proc Nat Acad Sci USA, 1998, 95: 4732–4737
O’Connor D H, Wittenberg G M, Wang S S-H. Graded bidirectional synaptic plasticity is composed of switch-like unitary events. Proc Nat Acad Sci USA, 2005, 102: 9679–9684
Suri M, Bichler O, Querlioz D, et al. Bio-inspired computing with binary stochastic CBRAM synapses. IEEE Trans Electron Dev, 2013, 60: 2402–2409
Li S Z, Zeng F, Chen C, et al. Synaptic plasticity and learning behaviours mimicked through Ag interface movement in an Ag/conducting polymer/Ta memristive system. J Mater Chem C, 2013, 1: 5292–5298
Yang Y, Chen B, Lu WD. Memristive physically evolving networks enabling the emulation of heterosynaptic plasticity. Adv Mater, 2015, 27: 7720–7727
Ielmini D. Filamentary-switching model in RRAM for time, energy and scaling projections. In: Proceedings of 2011 IEEE International Electron Devices Meeting (IEDM), Washington DC, 2011. 17.2.1–17.2.4
Belmonte A, Kim W, Chan B T, et al. A thermally stable and high-performance 90-nm-based 1T1R CBRAM cell. IEEE Trans Electron Dev, 2013, 60: 3690–3695
Russo U, Kamalanathan D, Ielmini D, et al. Study of multilevel programming in programmable metallization cell (PMC) memory. IEEE Trans Electron Dev, 2009, 56: 1040–1047
Wang K L, Alzate J G, Amiri P K. Low-power non-volatile spintronic memory: STT-RAM and beyond. J Phys D Appl Phys, 2013, 46: 074003
Augustine C, Mojumder N N, Fong X, et al. Spin-transfer torque MRAMs for low power memories: perspective and prospective. IEEE Sens J, 2012, 12: 756–766
Roy K, Fan D, Fong X, et al. Exploring spin transfer torque devices for unconventional computing. IEEE J Emerg Sel Top Circuits Syst, 2015, 5: 5–16
Devolder T, Hayakawa J, Ito K, et al. Single-shot time-resolved measurements of nanosecond-scale spin-transfer induced switching: stochastic versus deterministic aspects. Phys Rev Lett, 2008, 100: 057206
Zhang Y, Zhao W, Prenat G, et al. Electrical modeling of stochastic spin transfer torque writing in magnetic tunnel junctions for memory and logic applications. IEEE Trans Magn, 2013, 49: 4375–4378
Vincent A F, Larroque J, Locatelli N, et al. Spin-transfer torque magnetic memory as a stochastic memristive synapse for neuromorphic systems. IEEE Trans Biomed Circuits Syst, 2015, 9: 166–174
Zeng Z M, Amiri P K, Rowlands G, et al. Effect of resistance-area product on spin-transfer switching in MgO-based magnetic tunnel junction memory cells. Appl Phys Lett, 2011, 98: 072512
Zhou P, Zhao B, Yang J, et al. Energy reduction for STT-RAM using early write termination. In: Digest of Technical Papers of 2009 IEEE/ACM International Conference on Computer-Aided Design, San Jose, 2009. 264–268
Ovshinsky S R. Reversible electrical switching phenomena in disordered structures. Phys Rev Lett, 1968, 21: 1450–1453
Lai S. Current status of the phase change memory and its future. In: Technical Digest of IEEE International Electron Devices Meeting, Washington DC, 2003. 10.1.1–10.1.4
Lankhorst M H R, Ketelaars B W S M M, Wolters R A M. Low-cost and nanoscale non-volatile memory concept for future silicon chips. Nat Mater, 2005, 4: 347–352
Park J-B, Park G-S, Baik H-S, et al. Phase-change behavior of stoichiometric Ge2Sb2Te5 in phase-change random access memory. J Electrochem Soc, 2007, 154: H139–H141
Loke D, Lee T H, Wang W J, et al. Breaking the speed limits of phase-change memory. Science, 2012, 336: 1566–1569
Ovshinsky S R, Pashmakov B. Innovation providing new multiple functions in phase-change materials to achieve cognitive computing. MRS Proc, 2003, 803
Suri M, Bichler O, Querlioz D, et al. Phase change memory as synapse for ultra-dense neuromorphic systems: application to complex visual pattern extraction. In: Proceedings of 2011 IEEE International Electron Devices Meeting (IEDM), Washington DC, 2011. 4.4.1–4.4.4
Jackson B L, Rajendran B, Corrado G S, et al. Nanoscale electronic synapses using phase change devices. J Emerg Technol Comput Syst, 2013, 9: 12:1–12:20
Eryilmaz S B, Kuzum D, Jeyasingh R, et al. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front Neurosci, 2014, 8: 1–11
Hasler P, Diorio C, Minch B A, et al. Single transistor learning synapse with long term storage. In: Proceedings of 1995 IEEE International Symposium on Circuits and Systems, Seattle, 1995. 3: 1660–1663
Merolla P, Arthur J, Akopyan F, et al. A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm. In: Proceedings of 2011 IEEE Custom Integrated Circuits Conference (CICC), San Jose, 2011. 1–4
Seo J, Brezzo B, Liu Y, et al. A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons. In: Proceedings of 2011 IEEE Custom Integrated Circuits Conference (CICC), San Jose, 2011. 1–4
Likharev K K. Neuromorphic CMOL circuits. In: Proceedings of 2003 3rd IEEE Conference on Nanotechnology, San Francisco, 2003. 2: 339–342
Likharev K, Mayr A, Muckra I, et al. CrossNets: high-performance neuromorphic architectures for CMOL circuits. Ann N Y Acad Sci, 2003, 1006: 146–163
Likharev K K, Strukov D. B. CMOL: Devices, Circuits, and Architectures. In: Cuniberti G, Richter K, Fagas G, eds. Introducing Molecular Electronics. Berlin/Heidelberg: Springer, 2006. 447–477
Feldheim D L, Keating C D. Self-assembly of single electron transistors and related devices. Chem Soc Rev, 1998, 27: 1–12
Ma X, Strukov D B, Lee J H, et al. Afterlife for silicon: CMOL circuit architectures. In: Proceedings of 2005 5th IEEE Conference on Nanotechnology, Nagoya, 2005. 175–178
Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors. Nature, 1986, 323: 533–536
Maass W. Networks of spiking neurons: the third generation of neural network models. Neural Netw, 1997, 10: 1659–1671
Hodgkin A, Huxley A. A quantitative description of membrane current and its application to conduction and excitation in nerve. Bull Math Biol, 1990, 52: 25–71
O’Reilly R C. Biologically based computational models of high-level cognition. Science, 2006, 314: 91–94
Herz A V M, Gollisch T, Machens C K, et al. Modeling single-neuron dynamics and computations: a balance of detail and abstraction. Science, 2006, 314: 80–85
Brüderle D, Petrovici M A, Vogginger B, et al. A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems. Biol Cybern, 2011, 104: 263–296
Arthur J V, Boahen K. Silicon-neuron design: a dynamical systems approach. IEEE Trans Circuits Syst I-Regul Pap, 2011, 58: 1034–1043
Rachmuth G, Poon C-S. Transistor analogs of emergent iono-neuronal dynamics. HFSP J, 2008, 2: 156–166
Mead C. Analog VLSI and Neural Systems. Boston: Addison-Wesley Longman Publishing Co., Inc., 1989. 179–186
Pickett M D, Medeiros-Ribeiro G, Williams R S. A scalable neuristor built with Mott memristors. Nat Mater, 2013, 12: 114–117
Park S, Noh J, Choo M-L, et al. Nanoscale RRAM-based synaptic electronics: toward a neuromorphic computing device. Nanotechnology, 2013, 24: 384009