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Volumn 2016-May, Issue , 2016, Pages 118-123

Neuromorphic architectures with electronic synapses

Author keywords

Cognitive computing; device variability; monolithic integration; neuromorphic hardware; nonvolatile memory; phase change memory; resistive switching memory

Indexed keywords

ALGORITHMS; DATA STORAGE EQUIPMENT; DIGITAL STORAGE; ENERGY EFFICIENCY; LEARNING SYSTEMS; MONOLITHIC INTEGRATED CIRCUITS; NEURAL NETWORKS; NONVOLATILE STORAGE; PHASE CHANGE MEMORY;

EID: 84973869304     PISSN: 19483287     EISSN: 19483295     Source Type: Conference Proceeding    
DOI: 10.1109/ISQED.2016.7479186     Document Type: Conference Paper
Times cited : (37)

References (68)
  • 1
    • 80255127113 scopus 로고    scopus 로고
    • Neuromorphic Silicon Neuron Circuits
    • G. Indiveri et al., "Neuromorphic Silicon Neuron Circuits," Front. Neurosci., vol. 5, 2011.
    • (2011) Front. Neurosci , vol.5
    • Indiveri, G.1
  • 2
    • 84860660887 scopus 로고    scopus 로고
    • On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex
    • C. Ramos et al., "On spike-timing-dependent-plasticity, memristive devices, and building a self-learning visual cortex," Front. Neurosci., vol. 5, 2011.
    • (2011) Front. Neurosci , vol.5
    • Ramos, C.1
  • 3
    • 73349116544 scopus 로고    scopus 로고
    • Towards a mathematical theory of cortical micro-circuits
    • D. George et al., "Towards a mathematical theory of cortical micro-circuits," PLoS Comput. Biol., vol. 5, no. 10, 2009.
    • (2009) PLoS Comput. Biol , vol.5 , Issue.10
    • George, D.1
  • 4
    • 84930630277 scopus 로고    scopus 로고
    • Deep learning
    • Y. LeCun et al., "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.
    • (2015) Nature , vol.521 , Issue.7553 , pp. 436-444
    • LeCun, Y.1
  • 5
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • G. Hinton, S. Osindero and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural Comp., vol. 18, no. 7, pp. 1527-1554, 2006.
    • (2006) Neural Comp , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.1    Osindero, S.2    Teh, Y.-W.3
  • 7
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • G. E. Hinton and R.R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, 2006.
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 8
    • 69349090197 scopus 로고    scopus 로고
    • Learning deep architectures for AI
    • Y. Bengio, "Learning deep architectures for AI," Found. Trends. Mach. Learn., vol. 2, pp. 1-127.
    • Found. Trends. Mach. Learn , vol.2 , pp. 1-127
    • Bengio, Y.1
  • 10
    • 76749113376 scopus 로고    scopus 로고
    • Statistically optimal perception and learning: From behavior to neural representations
    • J. Fiser, P. Berkes, G. Orbán, and M. Lengyel, "Statistically optimal perception and learning: from behavior to neural representations," Trends in Cognitive Sciences, vol. 14, no. 3, pp. 119-130, 2010.
    • (2010) Trends in Cognitive Sciences , vol.14 , Issue.3 , pp. 119-130
    • Fiser, J.1    Berkes, P.2    Orbán, G.3    Lengyel, M.4
  • 11
    • 84965118275 scopus 로고    scopus 로고
    • Backpropagation for energy-efficient neuromorphic computing
    • Montreal
    • S. Esser et al., "Backpropagation for energy-efficient neuromorphic computing", NIPS, Montreal, 2015.
    • (2015) NIPS
    • Esser, S.1
  • 12
    • 84874143086 scopus 로고    scopus 로고
    • 65k-neuron integrate-and-fire array transceiver with address-event reconfigurable synaptic routing
    • 28-30 Nov
    • T. Yu et al., "65k-neuron integrate-and-fire array transceiver with address-event reconfigurable synaptic routing," BioCAS, IEEE, pp.21-24, 28-30 Nov. 2012.
    • (2012) BioCAS, IEEE , pp. 21-24
    • Yu, T.1
  • 13
    • 84920548116 scopus 로고    scopus 로고
    • A 65k-neuron 73-mevents/s 22-pj/event asynchronous micro-pipelined integrate-and-fire array transceiver
    • Oct
    • J. Park et al., "A 65k-neuron 73-mevents/s 22-pj/event asynchronous micro-pipelined integrate-and-fire array transceiver," BioCAS, IEEE, Oct. 2014.
    • (2014) BioCAS, IEEE
    • Park, J.1
  • 14
    • 84905915006 scopus 로고    scopus 로고
    • A million spiking-neuron integrated circuit with a scalable communication network and interface
    • P. A. Merolla et al., "A million spiking-neuron integrated circuit with a scalable communication network and interface," Science, vol. 345, no. 6197, pp. 668-673, 2014.
    • (2014) Science , vol.345 , Issue.6197 , pp. 668-673
    • Merolla, P.A.1
  • 15
    • 84880713240 scopus 로고    scopus 로고
    • Spatiotemporal spike pattern classification in neuromorphic systems
    • Springer Berlin Heidelberg
    • S. Sheik, M. Pfeiffer, F. Stefanini, G. Indiveri, "Spatiotemporal spike pattern classification in neuromorphic systems," Biomimetic and Biohybrid Systems, Springer Berlin Heidelberg, pp. 262-273, 2013.
    • (2013) Biomimetic and Biohybrid Systems , pp. 262-273
    • Sheik, S.1    Pfeiffer, M.2    Stefanini, F.3    Indiveri, G.4
  • 18
    • 78650972934 scopus 로고    scopus 로고
    • Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment
    • P. Berkes, G. Orbán, M. Lengyel, J Fiser, "Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment," Science, vol. 331, no. 6013, pp. 83-87, 2011.
    • (2011) Science , vol.331 , Issue.6013 , pp. 83-87
    • Berkes, P.1    Orbán, G.2    Lengyel, M.3    Fiser, J.4
  • 19
    • 84973880551 scopus 로고    scopus 로고
    • Nengo and the Neural Engineering Framework: From Spikes to Cognition. From
    • C. Eliasmith, and C. S. Terrence, "Nengo and the Neural Engineering Framework: From Spikes to Cognition." Cognitive Science Society, 2012.
    • (2012) Cognitive Science Society
    • Eliasmith, C.1    Terrence, C.S.2
  • 20
    • 81355133300 scopus 로고    scopus 로고
    • Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons
    • L. Buesing, J. Bill, B. Nessler, and W. Maass, "Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons," PLoS Comput. Biol., vol. 7, no. 11, 2011.
    • (2011) PLoS Comput. Biol , vol.7 , Issue.11
    • Buesing, L.1    Bill, J.2    Nessler, B.3    Maass, W.4
  • 22
    • 84888811418 scopus 로고    scopus 로고
    • Real-time classification and sensor fusion with a spiking deep belief network
    • P. O'Connor et al., "Real-time classification and sensor fusion with a spiking deep belief network," Front. Neurosci., vol. 7, 2013.
    • (2013) Front. Neurosci , vol.7
    • O'Connor, P.1
  • 23
    • 84897550107 scopus 로고    scopus 로고
    • Regularization of neural networks using dropconnect
    • L. Wan et al., "Regularization of neural networks using dropconnect," ICML, 2013.
    • (2013) ICML
    • Wan, L.1
  • 24
    • 84945951559 scopus 로고    scopus 로고
    • Event-driven contrastive divergence for spiking neuromorphic systems
    • E. Neftci et al., "Event-driven contrastive divergence for spiking neuromorphic systems," Front. Neurosci., vol. 7, 2013.
    • (2013) Front. Neurosci , vol.7
    • Neftci, E.1
  • 26
    • 84965179942 scopus 로고    scopus 로고
    • Synaptic sampling: A Bayesian approach to neural network plasticity and rewiring
    • D. Kappel, S. Habenschuss, R. Legenstein, and W. Maass, "Synaptic sampling: A Bayesian approach to neural network plasticity and rewiring," NIPS, 2015.
    • (2015) NIPS
    • Kappel, D.1    Habenschuss, S.2    Legenstein, R.3    Maass, W.4
  • 28
    • 84867135575 scopus 로고    scopus 로고
    • Building high-level features using large scale unsupervised learning
    • Q. V. Le et al., "Building high-level features using large scale unsupervised learning," ICML, 2012.
    • (2012) ICML
    • Le, Q.V.1
  • 29
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky et al., "Imagenet classification with deep convolutional neural networks," NIPS, 2012.
    • (2012) NIPS
    • Krizhevsky, A.1
  • 30
    • 71149105669 scopus 로고    scopus 로고
    • Large-scale deep unsupervised learning using graphics processors
    • R. Raina, A. Madhavan, and A. Y. Ng, "Large-scale deep unsupervised learning using graphics processors," ICML, 2009.
    • (2009) ICML
    • Raina, R.1    Madhavan, A.2    Ng, A.Y.3
  • 32
    • 84964031980 scopus 로고    scopus 로고
    • Device and system level design considerations for analog-non-volatile-memory based neuromorphic architectures
    • S. B. Eryilmaz, et al., "Device and system level design considerations for analog-non-volatile-memory based neuromorphic architectures," Electron Devices Meeting, 2015. IEDM'15 Technical Digest. IEEE International, pp. 4.1.1-4, 2015.
    • (2015) Electron Devices Meeting, 2015. IEDM'15 Technical Digest. IEEE International , pp. 411-414
    • Eryilmaz, S.B.1
  • 33
    • 84973915722 scopus 로고    scopus 로고
    • http://users.ece.gatech.edu/mrichard/ExascaleComputing StudyReports/exascale-final-report-100208.pdf
  • 34
    • 0025507283 scopus 로고
    • Neuromorphic electronic systems
    • C. Mead, "Neuromorphic electronic systems," Proc. IEEE, vol. 78, no. 10, pp. 1629-1636, 1990.
    • (1990) Proc. IEEE , vol.78 , Issue.10 , pp. 1629-1636
    • Mead, C.1
  • 35
    • 84900521434 scopus 로고    scopus 로고
    • Neurogrid: A mixed-analogdigital multichip system for large-scale neural simulations
    • B. V. Benjamin et al., "Neurogrid: A mixed-analogdigital multichip system for large-scale neural simulations," Proc. IEEE, vol. 102, no. 5, pp. 699-716, 2014.
    • (2014) Proc. IEEE , vol.102 , Issue.5 , pp. 699-716
    • Benjamin, B.V.1
  • 36
    • 33846098196 scopus 로고    scopus 로고
    • Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses
    • R. J. Vogelstein et al., "Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses," IEEE Trans. Neural Netw., vol. 18, no. 1, pp. 253-265, 2007.
    • (2007) IEEE Trans. Neural Netw , vol.18 , Issue.1 , pp. 253-265
    • Vogelstein, R.J.1
  • 37
    • 33244465845 scopus 로고    scopus 로고
    • A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity
    • G. Indiveri et al., "A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity," IEEE Trans. Neural Netw., vol. 17, no. 1, pp. 211-221, 2006.
    • (2006) IEEE Trans. Neural Netw , vol.17 , Issue.1 , pp. 211-221
    • Indiveri, G.1
  • 38
    • 84880823556 scopus 로고    scopus 로고
    • Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization
    • A.S. Cassidy et al., "Design of silicon brains in the nano-CMOS era: Spiking neurons, learning synapses and neural architecture optimization," Neural Networks, vol. 45, pp. 4-26, 2013.
    • (2013) Neural Networks , vol.45 , pp. 4-26
    • Cassidy, A.S.1
  • 39
    • 84963758832 scopus 로고    scopus 로고
    • Neuromorphic processing: A new frontier in scaling computer architecture
    • J. Gehlhaar, "Neuromorphic processing: A new frontier in scaling computer architecture," ASPLOS, 2014.
    • (2014) ASPLOS
    • Gehlhaar, J.1
  • 40
    • 56349083817 scopus 로고    scopus 로고
    • SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor
    • M.M. Khan et al., "SpiNNaker: mapping neural networks onto a massively-parallel chip multiprocessor," IJCNN, 2008.
    • (2008) IJCNN
    • Khan, M.M.1
  • 41
    • 80455156136 scopus 로고    scopus 로고
    • A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons
    • J. Seo et al., "A 45nm CMOS neuromorphic chip with a scalable architecture for learning in networks of spiking neurons," CICC, 2011.
    • (2011) CICC
    • Seo, J.1
  • 42
    • 0033740171 scopus 로고    scopus 로고
    • Point-to-point connectivity between neuromorphic chips using address events," Circuits and Systems II: Analog and Digital Signal Processing
    • K. Boahen, "Point-to-point connectivity between neuromorphic chips using address events," Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on, vol 47, no. 5, pp. 416-434, 2000.
    • (2000) IEEE Transactions on , vol.47 , Issue.5 , pp. 416-434
    • Boahen, K.1
  • 45
    • 79960834019 scopus 로고    scopus 로고
    • An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation
    • S. Yu et al., "An electronic synapse device based on metal oxide resistive switching memory for neuromorphic computation," IEEE Trans. Electron Devices, vol. 58, no. 8, pp. 2729-2737, 2011.
    • (2011) IEEE Trans. Electron Devices , vol.58 , Issue.8 , pp. 2729-2737
    • Yu, S.1
  • 46
    • 84861089198 scopus 로고    scopus 로고
    • Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing
    • D. Kuzum et al., "Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing," Nano Letters, vol. 12, no. 5, pp. 2179-2186, 2011.
    • (2011) Nano Letters , vol.12 , Issue.5 , pp. 2179-2186
    • Kuzum, D.1
  • 47
    • 79960642436 scopus 로고    scopus 로고
    • Short-term plasticity and long-term potentiation mimicked in single inorganic synapses
    • T. Ohno et al., "Short-term plasticity and long-term potentiation mimicked in single inorganic synapses," Nature Mat., vol. 10, no. 8, pp. 591-595, 2011.
    • (2011) Nature Mat , vol.10 , Issue.8 , pp. 591-595
    • Ohno, T.1
  • 48
    • 84866735724 scopus 로고    scopus 로고
    • A ferroelectric memristor
    • A. Chanthbouala et al.," A ferroelectric memristor," Nature Mat., vol. 11, no. 10, pp. 860-864, 2012.
    • (2012) Nature Mat , vol.11 , Issue.10 , pp. 860-864
    • Chanthbouala, A.1
  • 49
    • 84883517906 scopus 로고    scopus 로고
    • Synaptic electronics: Materials, devices and applications
    • D. Kuzum et al., "Synaptic electronics: materials, devices and applications," Nanotechnology, vol. 24, no. 38, pp. 382001, 2013.
    • (2013) Nanotechnology , vol.24 , Issue.38 , pp. 382001
    • Kuzum, D.1
  • 50
    • 84875158827 scopus 로고    scopus 로고
    • A Low Energy Oxide-Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation
    • S. Yu et al., "A Low Energy Oxide-Based Electronic Synaptic Device for Neuromorphic Visual Systems with Tolerance to Device Variation," Adv. Mat., vol. 25, no. 12, pp. 1774-1779, 2013.
    • (2013) Adv. Mat , vol.25 , Issue.12 , pp. 1774-1779
    • Yu, S.1
  • 51
    • 84933071588 scopus 로고    scopus 로고
    • Monolithic 3D integration of carbon nanotube FETs, resistive RAM, silicon FETs
    • M. M. Shulaker et al., "'Monolithic 3D integration of carbon nanotube FETs, resistive RAM, silicon FETs," IEDM, 2014.
    • (2014) IEDM
    • Shulaker, M.M.1
  • 53
    • 84991861809 scopus 로고    scopus 로고
    • Experimental demonstration of array-level learning with phase change synaptic devices
    • S. B. Eryilmaz et al., "Experimental demonstration of array-level learning with phase change synaptic devices," IEDM, 2013.
    • (2013) IEDM
    • Eryilmaz, S.B.1
  • 54
    • 84929223664 scopus 로고    scopus 로고
    • Electronic system with memristive synapses for pattern recognition
    • S. Park et al., "Electronic system with memristive synapses for pattern recognition," Scientific Reports, vol. 5, 2015.
    • (2015) Scientific Reports , vol.5
    • Park, S.1
  • 55
    • 84940931791 scopus 로고    scopus 로고
    • Experimental demonstration and tolerancing of a large-scale neural network (165, 000 synapses), using phase-change memory as the synaptic weight element
    • G. Burr et al., "Experimental demonstration and tolerancing of a large-scale neural network (165, 000 synapses), using phase-change memory as the synaptic weight element," IEDM, pp. 29-5, 2014.
    • (2014) IEDM , pp. 29-35
    • Burr, G.1
  • 56
    • 84929095672 scopus 로고    scopus 로고
    • Training and operation of an integrated neuromorphic network based on metal-oxide memristors
    • M. Prezioso et al., "Training and operation of an integrated neuromorphic network based on metal-oxide memristors," Nature, vol. 521, no. 7550, pp. 61-64, 2015.
    • (2015) Nature , vol.521 , Issue.7550 , pp. 61-64
    • Prezioso, M.1
  • 57
    • 84945955645 scopus 로고    scopus 로고
    • Technology-design co-optimization of resistive cross-point array for accelerating learning algorithms on chip
    • P. Y. Chen et al., "Technology-design co-optimization of resistive cross-point array for accelerating learning algorithms on chip," DATE, pp. 854-859, 2015.
    • (2015) DATE , pp. 854-859
    • Chen, P.Y.1
  • 58
    • 84857012303 scopus 로고    scopus 로고
    • Energy efficient programming of nanoelectronic synaptic devices for large-scale implementation of associative and temporal sequence learning
    • D. Kuzum, et al. "Energy efficient programming of nanoelectronic synaptic devices for large-scale implementation of associative and temporal sequence learning," IEDM, 2011.
    • (2011) IEDM
    • Kuzum, D.1
  • 59
    • 84864741849 scopus 로고    scopus 로고
    • Visual Pattern Extraction Using Energy-Efficient "2-PCM Synapse" Neuromorphic Architecture
    • O. Bichler et al., "Visual Pattern Extraction Using Energy-Efficient "2-PCM Synapse" Neuromorphic Architecture," IEEE Trans. Electron Devices, vol. 59, no. 8, pp. 2206-2214, 2012.
    • (2012) IEEE Trans. Electron Devices , vol.59 , Issue.8 , pp. 2206-2214
    • Bichler, O.1
  • 60
    • 84904736414 scopus 로고    scopus 로고
    • Stochastic learning in oxide binary synaptic device for neuromorphic computing
    • S. Yu et al., "Stochastic learning in oxide binary synaptic device for neuromorphic computing," Front. Neurosci., vol. 7, 2013.
    • (2013) Front. Neurosci , vol.7
    • Yu, S.1
  • 61
    • 84883491609 scopus 로고    scopus 로고
    • CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: Auditory (cochlea) and visual (retina) cognitive processing applications
    • M. Suri et al.," CBRAM devices as binary synapses for low-power stochastic neuromorphic systems: auditory (cochlea) and visual (retina) cognitive processing applications," IEDM, 2012.
    • (2012) IEDM
    • Suri, M.1
  • 63
    • 80054729052 scopus 로고    scopus 로고
    • Simulation of a memristor-based spiking neural network immune to device variations
    • D. Querlioz et al., Simulation of a memristor-based spiking neural network immune to device variations," IJCNN, 2011.
    • (2011) IJCNN
    • Querlioz, D.1
  • 64
    • 78650474133 scopus 로고    scopus 로고
    • A practical guide to training restricted Boltzmann machines
    • Dept. Comput. Sci., Univ. Toronto
    • G. E. Hinton, "A practical guide to training restricted Boltzmann machines," Tech. Rep. UTML TR 2010-003, Dept. Comput. Sci., Univ. Toronto, 2010.
    • (2010) Tech. Rep. UTML TR 2010-003
    • Hinton, G.E.1
  • 65
    • 84964045330 scopus 로고    scopus 로고
    • Comprehensive assessment of RRAMbased PUF for hardware security applications
    • A. Chen et al., "Comprehensive assessment of RRAMbased PUF for hardware security applications," IEDM, pp. 10.7.1-4, 2015.
    • (2015) IEDM , pp. 1071-1074
    • Chen, A.1
  • 66
    • 84905920654 scopus 로고    scopus 로고
    • Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array
    • S. B. Eryilmaz et al., "Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array," Front. Neurosci., vol. 8, 2014.
    • (2014) Front. Neurosci , vol.8
    • Eryilmaz, S.B.1
  • 67
    • 84964078185 scopus 로고    scopus 로고
    • Scaling-up resistive synaptic arrays for neuro-inspired architecture: Challenges and prospect
    • S. Yu et al., "Scaling-up resistive synaptic arrays for neuro-inspired architecture: Challenges and prospect," IEDM, pp. 17.3.1-4, 2015.
    • (2015) IEDM , pp. 1731-17314
    • Yu, S.1
  • 68
    • 84973905104 scopus 로고    scopus 로고
    • https://nano.stanford.edu/stanford-memory-trends


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