The DOI matches its Crossref record; 36 of the 37 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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.
Citations
Cites
Bi, G.-Q., & Poo, M.-M. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-24-10464.1998 · Cited by 4,744
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×
“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×
“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
Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity
Masquelier, T., & Thorpe, S. J. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030031 · Cited by 575
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 564
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×
“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
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
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
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 249
Rapid 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 218
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
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
Self-citationSimulation 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 213
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×
“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
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
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
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
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 136
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
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
Neural Network Implementation Using Fpga: Issues And Application
Muthuramalingam, A., Himavathi, S., & Srinivasan, E. (2008) · doi:10.5281/zenodo.1084401 · Cited by 96
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
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
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
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
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 57
Yu, S., & Wong, H. ‐. P. (2010) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2010.5703410 · Cited by 52
Appleby, P. A., & Elliott, T. (2006) · Neural Computation · doi:10.1162/neco.2006.18.10.2414 · Cited by 30
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
Self-citationInterface 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
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
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
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
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 1,104
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 1,006
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 618
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
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 419
Metadata
- 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
