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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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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
Self-citationStimulated Ionic Telegraph Noise in Filamentary Memristive Devices
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Modeling resistive switching materials and devices across scales
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Multilevel resistive switching in TiO2/Al2O3 bilayers at low temperature
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A Programmable Event-driven Architecture for Evaluating Spiking Neural Networks
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Antiferromagnetic skyrmion repulsion based artificial neuron device
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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
Self-citationAlleviating Conductance Nonlinearity via Pulse Shape Designs in TaOx Memristive Synapses
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Brain-Inspired technologies: Towards chips that think?
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Antiferromagnetic skyrmion based shape-configured leaky-integrate-fire neuron device
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On the impact of OxRAM-based synapses variability on convolutional neural networks performance
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Magnetic-Tunnel-Junction-Based True Random-Number Generator with Enhanced Generation Rate
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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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