Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses
Open accessAbstract
In this paper, we present an alternative approach to neuromorphic systems based on multilevel resistive memory synapses and deterministic learning rules. We demonstrate an original methodology to use conductive-bridge RAM (CBRAM) devices as, easy to program and low-power, binary synapses with stochastic learning rules. New circuit architecture, programming strategy, and probabilistic spike-timing dependent plasticity (STDP) learning rule for two different CBRAM configurations with-selector (1T-1R) and without-selector (1R) are proposed. We show two methods (intrinsic and extrinsic) for implementing probabilistic STDP rules. Fully unsupervised learning with binary synapses is illustrated through two example applications: 1) real-time auditory pattern extraction (inspired from a 64-channel silicon cochlea emulator); and 2) visual pattern extraction (inspired from the processing inside visual cortex). High accuracy (audio pattern sensitivity > 2, video detection rate > 95%) and low synaptic-power dissipation (audio 0.55 μW, video 74.2 μW) are shown. The robustness and impact of synaptic parameter variability on system performance are also analyzed.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744 · see citing works
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174 · cited 1× · see citing works
“potentiation (LTP) and multiple high-resistance states for long term depression (LTD) [19],[20].”
BackgroundA 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor
Lichtsteiner, P., Posch, C., & Delbruck, T. (2008) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2007.914337 · Cited by 2,485 · cited 2× · see citing works
“format by the authors of [31] is presented to a 2-layered SNN.”
BackgroundNeuromorphic Silicon Neuron Circuits
Indiveri, G., Linares-Barranco, B., Hamilton, T. J., André, V. S., Etienne‐Cummings, R., Delbrück, T., Liu, S., Dudek, P., Hafliger, P. D., Renaud, S., et al. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00073 · Cited by 1,836 · see citing works
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex
Zamarreño-Ramos, C., Camuñas-Mesa, L. A., Pérez‐Carrasco, J. A., Masquelier, T., Serrano‐Gotarredona, T., & Linares-Barranco, B. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00026 · Cited by 558 · see citing works
Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity
Masquelier, T., & Thorpe, S. J. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030031 · Cited by 549 · see citing works
Spike-timing-dependent learning in memristive nanodevices
Snider, G. S. (2008) · 2008 IEEE International Symposium on Nanoscale Architectures · doi:10.1109/nanoarch.2008.4585796 · Cited by 341 · cited 1× · see citing works
“Several research groups are actively working on implementing bio-inspired synaptic behavior directly in hardware [3],[4].”
BackgroundSuri, M., Bichler, O., Querlioz, D., Cueto, O., Perniola, L., Sousa, V., Vuillaume, D., Gamrat, C., & DeSalvo, B. (2011) · 2011 International Electron Devices Meeting · doi:10.1109/iedm.2011.6131488 · Cited by 314 · see citing works
Self-citationAER EAR: A Matched Silicon Cochlea Pair With Address Event Representation Interface
Chan, V., Liu, S., & van Schaik, A. (2007) · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · doi:10.1109/tcsi.2006.887979 · Cited by 290 · see citing works
Conductive bridging RAM (CBRAM): an emerging non-volatile memory technology scalable to sub 20nm
Kund, M., Beitel, G., Pinnow, C. U., Roehr, T., Schumann, J., Symanczyk, R., Ufert, K. ‐., & Muller, G. (2006) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2005.1609463 · Cited by 252 · see citing works
Compact Modeling of Conducting-Bridge Random-Access Memory (CBRAM)
Yu, S., & Wong, H. ‐. P. (2011) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2011.2116120 · Cited by 239 · see citing works
Yu, S., Gao, B., Fang, Z., Yu, H., Kang, J., & Wong, H.-S. P. (2012) · 2012 International Electron Devices Meeting · doi:10.1109/iedm.2012.6479018 · Cited by 213 · see citing works
Visual Pattern Extraction Using Energy-Efficient “2-PCM Synapse” Neuromorphic Architecture
Bichler, O., Suri, M., Querlioz, D., Vuillaume, D., de Salvo, B., & Gamrat, C. (2012) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2012.2197951 · Cited by 213 · see citing works
Self-citationRapid Formation of Robust Auditory Memories: Insights from Noise
Agus, T. R., Thorpe, S. J., & Pressnitzer, D. (2010) · Neuron · doi:10.1016/j.neuron.2010.04.014 · Cited by 205 · see citing works
Simulation of a memristor-based spiking neural network immune to device variations
Querlioz, D., Bichler, O., & Gamrat, C. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033439 · Cited by 203 · see citing works
Self-citationRRAM-based synapse for neuromorphic system with pattern recognition function
Park, S., Kim, H., Choo, M., Noh, J., Sheri, A., Jung, S., Seo, K., Park, J., Kim, S., Lee, W., et al. (2012) · 2012 International Electron Devices Meeting · doi:10.1109/iedm.2012.6479016 · Cited by 191 · cited 1× · see citing works
“memory (CBRAM) and oxide based memory (OXRAM) have been shown as good candidates for emulation of synaptic plasticity and learning rules like spike-timing dependent plasticity (STDP) [5],[6],[7],[8],[9].”
BackgroundResistance-dependent amplitude of random telegraph-signal noise in resistive switching memories
Ielmini, D., Nardi, F., & Cagli, C. (2010) · Applied Physics Letters · doi:10.1063/1.3304167 · Cited by 180 · see citing works
Bichler, O., Querlioz, D., Thorpe, S. J., Bourgoin, J. ‐., & Gamrat, C. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.022 · Cited by 160 · see citing works
Self-citationSuri, M., Bichler, O., Querlioz, D., Palma, G., Vianello, E., Vuillaume, D., Gamrat, C., & DeSalvo, B. (2012) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2012.6479017 · Cited by 149 · see citing works
Self-citationPhysical aspects of low power synapses based on phase change memory devices
Suri, M., Bichler, O., Querlioz, D., Traoré, B., Cueto, O., Perniola, L., Sousa, V., Vuillaume, D., Gamrat, C., & de Salvo, B. (2012) · Journal of Applied Physics · doi:10.1063/1.4749411 · Cited by 142 · see citing works
Self-citationBuilding block of a programmable neuromorphic substrate: A digital neurosynaptic core
Arthur, J. V., Merolla, P. A., Akopyan, F., Alvarez-Icaza, R., Cassidy, A. S., Chandra, S., Esser, S. K., Imam, N., Risk, W. P., Rubin, D. B. D., et al. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252637 · Cited by 132 · see citing works
Demonstration of Conductive Bridging Random Access Memory (CBRAM) in logic CMOS process
Gopalan, C., Ma, Y., Gallo, T., Wang, J., Runnion, E. F., Saenz, J., Koushan, F. S., Blanchard, P., & Hollmer, S. (2010) · Solid-State Electronics · doi:10.1016/j.sse.2010.11.024 · Cited by 128 · see citing works
Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons
Goldberg, D. H., Cauwenberghs, G., & Andreou, A. G. (2001) · Neural Networks · doi:10.1016/s0893-6080(01)00057-0 · Cited by 118 · see citing works
Neural Network Implementation Using Fpga: Issues And Application
Muthuramalingam, A., Himavathi, S., & Srinivasan, E. (2008) · doi:10.5281/zenodo.1084401 · Cited by 96 · see citing works
Hardware-based weighted random pattern generation for boundary scan
Brglez, F., Gloster, C., & Kedem, G. (2003) · doi:10.1109/test.1989.82307 · Cited by 94 · see citing works
Functional abilities of a stochastic logic neural network
Kondo, Y., & Sawada, Y. (1992) · IEEE Transactions on Neural Networks · doi:10.1109/72.129416 · Cited by 81 · see citing works
Kuzum, D., Jeyasingh, R. G. D., & Wong, H. ‐. P. (2011) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2011.6131643 · Cited by 63 · see citing works
Convergence of stochastic learning in perceptrons with binary synapses
Senn, W., & Fusi, S. (2005) · Physical Review E · doi:10.1103/physreve.71.061907 · Cited by 57 · see citing works
Defect‐tolerant nanoelectronic pattern classifiers
Lee, J. H., & Likharev, K. K. (2007) · International Journal of Circuit Theory and Applications · doi:10.1002/cta.410 · Cited by 53 · see citing works
Yu, S., & Wong, H. ‐. P. (2010) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2010.5703410 · Cited by 52 · see citing works
On the impact of Ag doping on performance and reliability of GeS2-based conductive bridge memories
Longnos, F., Vianello, E., Cagli, C., Molas, G., Souchier, E., Blaise, P., Carabasse, C., Rodriguez, G., Jousseaume, V., de Salvo, B., et al. (2013) · Solid-State Electronics · doi:10.1016/j.sse.2013.02.013 · Cited by 28 · see citing works
Self-citationAppleby, P. A., & Elliott, T. (2006) · Neural Computation · doi:10.1162/neco.2006.18.10.2414 · Cited by 27 · see citing works
Interface Engineering of PCM for Improved Synaptic Performance in Neuromorphic Systems
Suri, M., Bichler, O., Hubert, Q., Perniola, L., Sousa, V., Jahan, C., Vuillaume, D., Gamrat, C., & DeSalvo, B. (2012) · IEEE International Memory Workshop (IMW) · doi:10.1109/imw.2012.6213674 · Cited by 19 · see citing works
Self-citationPalma, G., Vianello, E., Cagli, C., Longnos, F., Dahmani, F., Molas, G., Reyboz, M., Blaise, P., & de Salvo, B. (2012) · IEEE International Memory Workshop (IMW) · doi:10.1109/imw.2012.6213680 · Cited by 15 · see citing works
Self-citationResistance Drift Model for Conductive-Bridge (CB) RAM by Filament Surface Relaxation
Choi, S., Ambrogio, S., Balatti, S., Nardi, F., & Ielmini, D. (2012) · IEEE International Memory Workshop (IMW) · doi:10.1109/imw.2012.6213666 · Cited by 12 · see citing works
On the impact of Ag doping on performance and reliability of GeS2-based Conductive Bridge Memories
Vianello, E., Cagli, C., Molas, G., Souchier, E., Blaise, P., Carabasse, C., Rodriguez, G., Jousseaume, V., de Salvo, B., Longnos, F., et al. (2012) · European Solid-State Device Research Conference · doi:10.1109/essderc.2012.6343387 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
1 of the 37 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Memory and Information Processing in Neuromorphic Systems
Indiveri, G., & Liu, S. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2444094 · Cited by 1,047 · see citing works
A Survey of Neuromorphic Computing and Neural Networks in HardwarePreprint
Schuman, C. D., Potok, T. E., Patton, R. M., Birdwell, J. D., Dean, M. E., Rose, G. S., & Plank, J. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.06963 · Cited by 861 · see citing works
Learning through ferroelectric domain dynamics in solid-state synapses
Boyn, S., Grollier, J., Lecerf, G., Xu, B., Locatelli, N., Fusil, S., Girod, S., Carrétéro, C., Garcia, K. L., Xavier, S., et al. (2017) · Nature Communications · doi:10.1038/ncomms14736 · Cited by 605 · see citing works
Woo, J., Moon, K., Song, J., Lee, S., Kwak, M., Park, J., & Hwang, H. (2016) · IEEE Electron Device Letters · doi:10.1109/led.2016.2582859 · Cited by 512 · see citing works
Memristors for Energy‐Efficient New Computing Paradigms
Jeong, D. S., Kim, K. M., Kim, S., Choi, B. J., & Hwang, C. S. (2016) · Advanced Electronic Materials · doi:10.1002/aelm.201600090 · Cited by 414 · see citing works
Spin-Transfer Torque Magnetic Memory as a Stochastic Memristive Synapse for Neuromorphic Systems
Vincent, A. F., Larroque, J., Locatelli, N., Ben Romdhane, N., Bichler, O., Gamrat, C., Zhao, W. S., Klein, J.-O., Galdin-Retailleau, S., & Querlioz, D. (2015) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2015.2414423 · Cited by 360 · see citing works
Self-citationRecent Progress in Three‐Terminal Artificial Synapses: From Device to System
Han, H., Yu, H., Wei, H., Gong, J., & Xu, W. (2019) · Small · doi:10.1002/smll.201900695 · Cited by 350 · see citing works
Magnetic skyrmion-based synaptic devices
Huang, Y., Kang, W., Zhang, X., Zhou, Y., & Zhao, W. S. (2017) · Nanotechnology · doi:10.1088/1361-6528/aa5838 · Cited by 314 · see citing works
Ielmini, D., & Ambrogio, S. (2019) · Nanotechnology · doi:10.1088/1361-6528/ab554b · Cited by 273 · see citing works
Towards Oxide Electronics: a Roadmap
Coll, M., Fontcuberta, J., Althammer, M., Bibès, M., Boschker, H., Calleja, A., Cheng, G., Cuoco, M., Dittmann, R., Dkhil, B., et al. (2019) · Applied Surface Science · doi:10.1016/j.apsusc.2019.03.312 · Cited by 315 · see citing works
Emerging Artificial Synaptic Devices for Neuromorphic Computing
Wan, Q., Sharbati, M. T., Erickson, J. R., Du, Y., & Xiong, F. (2019) · Advanced Materials Technologies · doi:10.1002/admt.201900037 · Cited by 303 · see citing works
Reservoir Computing Using Diffusive Memristors
Midya, R., Wang, Z., Asapu, S., Zhang, X., Rao, M., Song, W., Zhuo, Y., Upadhyay, N., Xia, Q., & Yang, J. J. (2019) · Advanced Intelligent Systems · doi:10.1002/aisy.201900084 · Cited by 279 · see citing works
Spiking Neural Networks Hardware Implementations and Challenges
Bouvier, M., Valentian, A., Mesquida, T., Rummens, F., Reyboz, M., Vianello, E., & Beigné, É. (2019) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3304103 · Cited by 274 · see citing works
Self-citationExploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware
Rathi, N., Chakraborty, I., Kosta, A. K., Sengupta, A., Ankit, A., Panda, P., & Roy, K. (2022) · ACM Computing Surveys · doi:10.1145/3571155 · Cited by 274 · see citing works
Ambrogio, S., Balatti, S., Milo, V., Carboni, R., Wang, Z.-Q., Calderoni, A., Ramaswamy, N., & Ielmini, D. (2016) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2016.2526647 · Cited by 256 · see citing works
Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses
Ambrogio, S., Ciocchini, N., Laudato, M., Milo, V., Pirovano, A., Fantini, P., & Ielmini, D. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00056 · Cited by 249 · see citing works
Nonvolatile Memory Materials for Neuromorphic Intelligent Machines
Jeong, D. S., & Hwang, C. S. (2018) · Advanced Materials · doi:10.1002/adma.201704729 · Cited by 244 · see citing works
Plasticity in memristive devices for spiking neural networks
Saïghi, S., Mayr, C., Serrano‐Gotarredona, T., Schmidt, H., Lecerf, G., Tomas, J., Grollier, J., Boyn, S., Vincent, A. F., Querlioz, D., et al. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00051 · Cited by 236 · see citing works
Self-citationRecent progress in analog memory-based accelerators for deep learning
Tsai, H.-Y. S., Ambrogio, S., Narayanan, P., Shelby, R. M., & Burr, G. W. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aac8a5 · Cited by 223 · see citing works
Prezioso, M., Mahmoodi, M. R., Bayat, F. M., Nili, H., Kim, H., Vincent, A., & Strukov, D. B. (2018) · Nature Communications · doi:10.1038/s41467-018-07757-y · Cited by 215 · see citing works
Srinivasan, G., Sengupta, A., & Roy, K. (2016) · Scientific Reports · doi:10.1038/srep29545 · Cited by 214 · see citing works
Vodenicarevic, D., Locatelli, N., Mizrahi, F. A., Friedman, J. S., Vincent, A. F., Romera, M., Fukushima, A., Yakushiji, K., Kubota, H., Yuasa, S., et al. (2017) · Physical Review Applied · doi:10.1103/physrevapplied.8.054045 · Cited by 207 · see citing works
Self-citationAnalog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning
Covi, E., Brivio, S., Serb, A., Prodromakis, T., Fanciulli, M., & Spiga, S. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00482 · Cited by 178 · see citing works
Pathways to efficient neuromorphic computing with non-volatile memory technologies
Chakraborty, I., Jaiswal, A., Saha, A. K., Gupta, S. K., & Roy, K. (2020) · Applied Physics Reviews · doi:10.1063/1.5113536 · Cited by 166 · see citing works
Pedretti, G., Milo, V., Ambrogio, S., Carboni, R., Bianchi, S., Calderoni, A., Ramaswamy, N., Spinelli, A. S., & Ielmini, D. (2017) · Scientific Reports · doi:10.1038/s41598-017-05480-0 · Cited by 165 · see citing works
Seo, S., Lee, J., Lee, H., Lee, H. W., Oh, S., Lee, J. J., Heo, K., & Park, J. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.9b00694 · Cited by 161 · see citing works
Device and materials requirements for neuromorphic computing
Islam, R., Li, H., Chen, P.-Y., Wan, W., Chen, H.-Y., Gao, B., Wu, H., Yu, S., Saraswat, K. C., & Wong, H. ‐. P. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aaf784 · Cited by 159 · see citing works
Nonstationary distributions and relaxation times in a stochastic model of memristor
Викторович, А. Н., Safonov, A. V., Krichigin, A. V., Kharcheva, A. A., Dubkov, A. A., Valenti, D., Guseinov, D. V., Belov, A. I., Mikhaylov, A. N., Carollo, A. C. M., et al. (2020) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ab684a · Cited by 159 · see citing works
Bioinspired Programming of Memory Devices for Implementing an Inference Engine
Querlioz, D., Bichler, O., Vincent, A. F., & Gamrat, C. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2437616 · Cited by 138 · see citing works
Self-citationMidya, R., Wang, Z., Asapu, S., Joshi, S., Li, Y., Zhuo, Y., Song, W., Jiang, H., Upadhyay, N. K., Rao, M., et al. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900060 · Cited by 132 · see citing works
Memristors Empower Spiking Neurons With Stochasticity
Al-Shedivat, M., Naous, R., Cauwenberghs, G., & Salama, K. N. (2015) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2015.2435512 · Cited by 128 · see citing works
Yang, Y., Wen, J., Guo, L., Wan, X., Du, P., Feng, P., Shi, Y., & Wan, Q. C. (2016) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.6b08515 · Cited by 128 · see citing works
Organic synaptic devices for neuromorphic systems
Sun, J., Fu, Y., & Wan, Q. C. (2018) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aacd99 · Cited by 122 · see citing works
Sokolov, A. S., Ali, M., Riaz, R., Abbas, Y., Ko, M. J., & Choi, C. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201807504 · Cited by 118 · see citing works
Exploring the Connection Between Binary and Spiking Neural Networks
Lu, S. K., & Sengupta, A. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00535 · Cited by 111 · see citing works
A compound memristive synapse model for statistical learning through STDP in spiking neural networks
Bill, J., & Legenstein, R. A. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00412 · Cited by 104 · see citing works
Mahmoodi, M. R., Prezioso, M., & Strukov, D. B. (2019) · Nature Communications · doi:10.1038/s41467-019-13103-7 · Cited by 101 · see citing works
Woo, J., Padovani, A., Moon, K., Kwak, M., Larcher, L., & Hwang, H. (2017) · IEEE Electron Device Letters · doi:10.1109/led.2017.2731859 · Cited by 103 · see citing works
“phase change memory [3], [4], spin-based memory [5], and resistive switching memory (RRAM) [6]–[11].”
BackgroundStochasticity Modeling in Memristors
Naous, R., Al-Shedivat, M., & Salama, K. N. (2015) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2015.2493960 · Cited by 102 · see citing works
A recipe for creating ideal hybrid memristive-CMOS neuromorphic processing systems
Chicca, E., & Indiveri, G. (2020) · Applied Physics Letters · doi:10.1063/1.5142089 · Cited by 93 · see citing works
Synaptic electronics and neuromorphic computing
Upadhyay, N. K., Joshi, S., & Yang, J. J. (2016) · Science China Information Sciences · doi:10.1007/s11432-016-5565-1 · Cited by 97 · see citing works
Chen, Y., Zhou, Y., Zhuge, F., Tian, B., Yan, M., Li, Y., He, Y., & Miao, X. S. (2019) · npj 2D Materials and Applications · doi:10.1038/s41699-019-0114-6 · Cited by 96 · see citing works
Wang, Z., Ambrogio, S., Balatti, S., & Ielmini, D. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00438 · Cited by 93 · see citing works
Payvand, M., Nair, M. V., Müller, L. K., & Indiveri, G. (2018) · Faraday Discussions · doi:10.1039/c8fd00114f · Cited by 85 · see citing works
Ryu, J.-H., & Kim, S. (2020) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110236 · Cited by 87 · see citing works
Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing
Wang, R., Shi, T., Zhang, X., Wang, W., Wei, J., Lü, J., Zhao, X., Wu, Z., Cao, R., Long, S., et al. (2018) · Materials · doi:10.3390/ma11112102 · Cited by 85 · see citing works
Emerging memory technologies for neuromorphic computing
Kim, C.-H., Lim, S., Woo, S. Y., Kang, W.-M., Seo, Y.-T., Lee, S., Lee, S., Kwon, D., Oh, S., Noh, Y., et al. (2018) · Nanotechnology · doi:10.1088/1361-6528/aae975 · Cited by 82 · see citing works
Rahmani, M. K., Ismail, M., Mahata, C., & Kim, S. (2020) · Results in Physics · doi:10.1016/j.rinp.2020.103325 · Cited by 80 · see citing works
Truong, S. N., Ham, S.-J., & Min, K. (2014) · Nanoscale Research Letters · doi:10.1186/1556-276x-9-629 · Cited by 80 · see citing works
Stable Self-Assembled Atomic-Switch Networks for Neuromorphic Applications
Bose, S. K., Mallinson, J. B., Gazoni, R. M., & Brown, S. A. (2017) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2017.2766063 · Cited by 77 · see citing works
Two-Dimensional Near-Atom-Thickness Materials for Emerging Neuromorphic Devices and Applications
Ko, T., Li, H., Mofid, S. A., Yoo, C., Okogbue, E., Han, S. S., Shawkat, M. S., Krishnaprasad, A., Islam, M. M., Dev, D., et al. (2020) · iScience · doi:10.1016/j.isci.2020.101676 · Cited by 78 · see citing works
Stochastic Electronics: A Neuro-Inspired Design Paradigm for Integrated Circuits
Hamilton, T. J., Afshar, S., André, V. S., & Tapson, J. C. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2310713 · Cited by 71 · see citing works
Srinivasan, G., & Roy, K. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00189 · Cited by 68 · see citing works
On Practical Issues for Stochastic STDP Hardware With 1-bit Synaptic Weights
Yousefzadeh, A., Stromatias, E., Soto, M., Serrano‐Gotarredona, T., & Linares-Barranco, B. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00665 · Cited by 66 · see citing works
An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting
Bernert, M., & Yvert, B. (2018) · International Journal of Neural Systems · doi:10.1142/s0129065718500594 · Cited by 64 · see citing works
Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses
Lin, Y., Bennett, C. H., Cabaret, T., Vodenicarevic, D., Chabi, D., Querlioz, D., Jousselme, B., Derycke, V., & Klein, J. (2016) · Scientific Reports · doi:10.1038/srep31932 · Cited by 62 · see citing works
Self-citationMemristive computing devices and applications
Zidan, M. A., Chen, A., Indiveri, G., & Lu, W. D. (2017) · Journal of Electroceramics · doi:10.1007/s10832-017-0103-0 · Cited by 62 · see citing works
Roadmap on material-function mapping for photonic-electronic hybrid neural networks
Miscuglio, M., Adam, G. C., Kuzum, D., & Sorger, V. J. (2019) · APL Materials · doi:10.1063/1.5109689 · Cited by 58 · see citing works
Kim, T.-Y., Baek, G., Yang, S., Yang, J., Yoon, K. S., Kim, S. G., Lee, J. Y., Im, H. S., & Hong, J. P. (2018) · Scientific Reports · doi:10.1038/s41598-018-26997-y · Cited by 56 · see citing works
A 2D material-based floating gate device with linear synaptic weight update
Park, E., Kim, M., Kim, T. S., Kim, I. S., Park, J., Kim, J., Jeong, Y., Lee, S., Kim, I., Park, J., et al. (2020) · Nanoscale · doi:10.1039/d0nr07403a · Cited by 54 · see citing works
New progresses in swarm intelligence-based computation
Duan, H., & Luo, Q. (2015) · International Journal of Bio-Inspired Computation · doi:10.1504/ijbic.2015.067981 · Cited by 51 · see citing works
A Review of Graphene‐Based Memristive Neuromorphic Devices and Circuits
Walters, B., Jacob, M. V., Amirsoleimani, A., & Azghadi, M. R. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202300136 · Cited by 49 · see citing works
Spin-transfer torque magnetic memory as a stochastic memristive synapse
Vincent, A. F., Larroque, J., Zhao, W. S., Romdhane, N. B., Bichler, O., Gamrat, C., Klein, J., Galdin-Retailleau, S., & Querlioz, D. (2014) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2014.6865325 · Cited by 49 · see citing works
Self-citationKang, D., Kim, J. H., Oh, S., Park, H., Dugasani, S. R., Kang, B., Choi, C., Choi, R., Lee, S., Park, S. H., et al. (2019) · Advanced Science · doi:10.1002/advs.201901265 · Cited by 49 · see citing works
MoS 2 ‐based Charge‐trapping synaptic device with electrical and optical modulated conductance
Zhang, M., Fan, Z., Jiang, X., Zhu, H., Chen, L., Xia, Y., Yin, J., Liu, X., Sun, Q., & Zhang, D. W. (2020) · Nanophotonics · doi:10.1515/nanoph-2019-0548 · Cited by 49 · see citing works
Convolutional Neural Networks Based on RRAM Devices for Image Recognition and Online Learning Tasks
Dong, Z., Zhou, Z., Li, Z., Liu, C., Huang, P. C., Liu, L., Liu, X., & Kang, J. (2018) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2018.2882779 · Cited by 49 · see citing works
Recent trends in neuromorphic engineering
Soman, S., Jayadeva, J., & Suri, M. (2016) · Big Data Analytics · doi:10.1186/s41044-016-0013-1 · Cited by 48 · see citing works
Self-citationBuilding Brain-Inspired Computing Systems: Examining the Role of Nanoscale Devices
Nandakumar, S. R., Kulkarni, S., Babu, A. V., & Rajendran, B. (2018) · IEEE Nanotechnology Magazine · doi:10.1109/mnano.2018.2845078 · Cited by 47 · see citing works
Chang, Y., Zhou, F., Fowler, B. W., Chen, Y., Hsieh, C., Guckert, L., Swartzlander, E. E., & Lee, J. C. (2017) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2017.2699679 · Cited by 42 · see citing works
Recent progress on two-dimensional neuromorphic devices and artificial neural network
Tian, C., Wei, L., Li, Y., & Jiang, J. (2021) · Current Applied Physics · doi:10.1016/j.cap.2021.08.014 · Cited by 42 · see citing works
Brivio, S., Conti, D. V., Nair, M. V., Frascaroli, J., Covi, E., Ricciardi, C., Indiveri, G., & Spiga, S. (2018) · Nanotechnology · doi:10.1088/1361-6528/aae81c · Cited by 41 · see citing works
Exploiting Oxide Based Resistive RAM Variability for Bayesian Neural Network Hardware Design
Malhotra, A., Lu, S. K., Yang, K., & Sengupta, A. (2020) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2020.2982819 · Cited by 39 · see citing works
Self-organization of an inhomogeneous memristive hardware for sequence learning
Payvand, M., Moro, F., Nomura, K., Dalgaty, T., Vianello, E., Nishi, Y., & Indiveri, G. (2022) · Nature Communications · doi:10.1038/s41467-022-33476-6 · Cited by 39 · see citing works
Self-citationDutta, M., Maikap, S., & Qiu, J.-T. T. (2018) · Advanced Electronic Materials · doi:10.1002/aelm.201800288 · Cited by 36 · see citing works
ReRAM‐Based Neuromorphic Computing
Indiveri, G., Linn, E., & Ambrogio, S. (2016) · Wiley eBooks · doi:10.1002/9783527680870.ch25 · Cited by 36 · see citing works
Investigation of the potentialities of Vertical Resistive RAM (VRRAM) for neuromorphic applications
Piccolboni, G., Molas, G., Portal, J. M., Coquand, R., Bocquet, M., Garbin, D., Vianello, E., Carabasse, C., Delaye, V., Pellissier, C., et al. (2015) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2015.7409717 · Cited by 35 · see citing works
Self-citationAnalogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices
Zahari, F., Pérez, E., Mahadevaiah, M. K., Kohlstedt, H., Wenger, C., & Ziegler, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-71334-x · Cited by 35 · see citing works
Bengel, C., Cüppers, F., Payvand, M., Dittmann, R., Waser, R. M., Hoffmann‐Eifert, S., & Menzel, S. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.661856 · Cited by 35 · see citing works
On-Chip Error-Triggered Learning of Multi-Layer Memristive Spiking Neural Networks
Payvand, M., Fouda, M. E., Kurdahi, F. J., Eltawil, A. M., & Neftci, E. O. (2020) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2020.3040248 · Cited by 31 · see citing works
Suri, M., & Parmar, V. (2015) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2015.2441112 · Cited by 33 · see citing works
Self-citationAjayan, J., Nirmal, D., I.V, B. K. J., & Sreejith, S. (2022) · Microelectronics Journal · doi:10.1016/j.mejo.2022.105634 · Cited by 33 · see citing works
Synaptic learning and memory functions in SiO 2 :Ag/TiO 2 based memristor devices
Li, D., Ilyas, N., Li, C., Jiang, X., Jiang, Y., & Li, W. (2020) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ab70c9 · Cited by 32 · see citing works
Survey of Stochastic-Based Computation Paradigms
Alawad, M., & Lin, M. (2016) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2016.2598726 · Cited by 31 · see citing works
Silicon compatible Sn-based resistive switching memory
Sonde, S. S., Chakrabarti, B., Liu, Y., Sasikumar, K., Lin, J., Stan, L., Divan, R., Ocola, L. E., Rosenmann, D., Choudhury, P., et al. (2018) · Nanoscale · doi:10.1039/c8nr01540f · Cited by 31 · see citing works
Device and Circuit Interaction Analysis of Stochastic Behaviors in Cross-Point RRAM Arrays
Li, H., Huang, P., Gao, B., Liu, X., Kang, J., & Wong, H. ‐. P. (2017) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2017.2766046 · Cited by 30 · see citing works
Zayer, F., Dghais, W., Benabdeladhim, M., & Hamdi, B. (2019) · AEU - International Journal of Electronics and Communications · doi:10.1016/j.aeue.2019.01.003 · Cited by 30 · see citing works
Jang, J. T., Kim, D., Choi, W. S., Choi, S., Kim, D. M., Kim, Y., & Kim, D. H. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.0c00499 · Cited by 29 · see citing works
Spintronic Devices as Key Elements for Energy-Efficient Neuroinspired Architectures
Locatelli, N., Grollier, J., Querlioz, D., Vincent, A. F., Mizrahi, F. A., Friedman, J. S., Vodenicarevic, D., Kim, J.-V., Klein, J., & Zhao, W. S. (2015) · Design, Automation, and Test in Europe · doi:10.7873/date.2015.1117 · Cited by 29 · see citing works
Self-citationStimulated Ionic Telegraph Noise in Filamentary Memristive Devices
Brivio, S., Frascaroli, J., Covi, E., & Spiga, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-41497-3 · Cited by 29 · see citing works
Modeling resistive switching materials and devices across scales
Ambrogio, S., Magyari‐Köpe, B., Onofrio, N., Islam, M. M., Duncan, D. L., Nishi, Y., & Strachan, A. H. (2017) · Journal of Electroceramics · doi:10.1007/s10832-017-0093-y · Cited by 28 · see citing works
Multilevel resistive switching in TiO2/Al2O3 bilayers at low temperature
Andreeva, N. V., Ivanov, A. S., & Petrov, A. (2018) · AIP Advances · doi:10.1063/1.5019570 · Cited by 28 · see citing works
A Programmable Event-driven Architecture for Evaluating Spiking Neural Networks
Roy, A., Venkataramani, S., Gala, N., Sen, S., Veezhinathan, K., & Raghunathan, A. (2017) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1109/islped.2017.8009176 · Cited by 26 · see citing works
Antiferromagnetic skyrmion repulsion based artificial neuron device
Bindal, N., Ang, C. C. I., Lew, W. S., & Kaushik, B. K. (2021) · Nanotechnology · doi:10.1088/1361-6528/abe261 · Cited by 26 · see citing works
Neuromorphic hybrid RRAM-CMOS RBM architecture
Suri, M., Parmar, V., Kumar, A., Querlioz, D., & Alibart, F. (2015) · Non-Volatile Memory Technology Symposium · doi:10.1109/nvmts.2015.7457484 · Cited by 24 · see citing works
Self-citationAlleviating Conductance Nonlinearity via Pulse Shape Designs in TaOx Memristive Synapses
Li, S.-J., Dong, B.-Y., Wang, B., Li, Y., Sun, H.-J., He, Y.-H., Xu, N., & Miao, X.-S. (2019) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2018.2876065 · Cited by 14 · see citing works
Brain-Inspired technologies: Towards chips that think?
de Salvo, B. (2018) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2018.8310165 · Cited by 23 · see citing works
Antiferromagnetic skyrmion based shape-configured leaky-integrate-fire neuron device
Bindal, N., Raj, R. K., & Kaushik, B. K. (2022) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ac71e4 · Cited by 22 · see citing works
On the impact of OxRAM-based synapses variability on convolutional neural networks performance
Garbin, D., Vianello, E., Bichler, O., Azzaz, M., Rafhay, Q., Candelier, P., Gamrat, C., Ghibaudo, G., DeSalvo, B., & Perniola, L. (2015) · IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH) · doi:10.1109/nanoarch.2015.7180611 · Cited by 20 · see citing works
Self-citationDynamic resistive switching devices for neuromorphic computing
Wu, Y., Wang, X., & Lu, W. D. (2021) · Semiconductor Science and Technology · doi:10.1088/1361-6641/ac41e4 · Cited by 20 · see citing works
Magnetic-Tunnel-Junction-Based True Random-Number Generator with Enhanced Generation Rate
Chen, X., Zhang, J., & Xiao, J. (2022) · Physical Review Applied · doi:10.1103/physrevapplied.18.l021002 · Cited by 20 · see citing works
- Authors
- Suri, M., Querlioz, D., Bichler, O., Palma, G., Vianello, E., Vuillaume, D., Gamrat, C., & DeSalvo, B.
- Affiliations
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université Paris-Sud (FR)
- Centre National de la Recherche Scientifique (FR)
- Venue
- IEEE Transactions on Electron Devices
- Published
- 2013-07
- Volume
- 60
- Issue
- 7
- Pages
- 2402-2409
- ISSN
- 0018-9383 (ISSN-L)
- 1557-9646
- OpenAlex
- W2060694887
- Cited by
- 229
- References
- 37
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 2 · 2023: 12 · 2022: 13 · 2021: 14 · 2020: 30 · 2019: 26 · 2018: 38 · 2017: 30 · 2016: 20 · 2015: 27 · 2014: 9 · 2013: 1
- FWCI
- 11.40
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE Transactions on Electron Devices (publishedVersion)
- LillOA (Université de Lille (University Of Lille)) (submittedVersion, other-oa)
- HAL (submittedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Keywords
- stochastic computing, conductive bridge RAM, neuromorphic systems, binary synapses, unsupervised learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
