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.
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Cites
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Nanoscale Memristor Device as Synapse in Neuromorphic Systems
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“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
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“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
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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
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“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
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Conductive bridging RAM (CBRAM): an emerging non-volatile memory technology scalable to sub 20nm
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1 of the 37 references couldn’t be matched to a known paper.
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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
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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
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