The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
A neuromorphic visual system using RRAM synaptic devices with Sub-pJ energy and tolerance to variability: Experimental characterization and large-scale modeling
Abstract
We report the use of metal oxide resistive switching memory (RRAM) as synaptic devices for a neuromorphic visual system. At the device level, we experimentally characterized the gradual resistance modulation of RRAM by hundreds of identical pulses. As compared with phase change memory (PCM) reported recently in [1,2], >100×-1000× energy consumption reduction was achieved in RRAM as synaptic devices (<;1 pJ per spike). Based on the experimental results, we developed a stochastic model to quantify the device switching dynamics. At the system level, we simulated the performance of image orientation selectivity on a neuromorphic visual system which consists of 1,024 CMOS neuron circuits and 16,348 RRAM synaptic devices. It was found that the system can tolerate the temporal and spatial variability which are commonly present in RRAM devices, suggesting the feasibility of large-scale hardware implementation of neuromorphic system using RRAM synaptic devices.
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“However, many studies have reported that only two discrete states are demonstrated during potentiation, whereas depression demonstrates multiple states [9-11].”
From Linking Conductive Filament Properties and Evolution to Synaptic Behavior of RRAM Devices for Neuromorphic Applications (2017) · cites this paper as [11]
Citations
Cites
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Self-citationOn Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex
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A SPICE Compact Model of Metal Oxide Resistive Switching Memory With Variations
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- yu, 2011, IEDM, 413
- kuzum, 2011, IEDM, 693
- suri, 2011, IEDM, 79
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Items 201–213 of 213
Властивості матеріалів для резистивної пам’яті з довільним доступом
Zalevskyi, D. V. (2023) · doi:10.31812/123456789/7070 · Cited by 1
Pattern Classification with Memristive Crossbar Circuits
Strukov, D. B. (2016)
Ma, Y., Yu, A., Zhang, Y., Wang, Z., Ding, X., Feng, Y., & Liu, L. (2019) · doi:10.1109/iwofc48002.2019.9078438
Bio-inspired learning and hardware acceleration with emerging memories
Kulkarni, S. (2019)
A Compact DC I-V Model for ReRAM
Syamal, B., Li, J., Chiah, S. B., & Zhou, X. (2020) · IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) · doi:10.1109/ipfa49335.2020.9260807
Memristor-based Mixed Training Scheme with Multi-level Sparsification
Xiong, F., Hu, X., & Zhou, Y. (2023) · IEEE International Conference on Memristive Computing and Applications (ICMCA) · doi:10.1109/icmca59770.2023.10481481
Invisible ZnO-based Memristor Universal Logic for Reconfigurable ElectronicsPreprint
Chen, H., Chandrasekaran, S., Gao, S., Georgiadou, D. G., Hajiabadi, Z., & Simanjuntak, F. M. (2024) · doi:10.36227/techrxiv.171259797.74935979/v1
Cho, J. R., Park, J., Myoung, S. J., Yang, T. J., Kim, C., Bae, J., Choi, S., Kim, D. M., Song, I., & Kim, D. H. (2025) · Solid-State Electronics · doi:10.1016/j.sse.2025.109084
Resistive memory co-design in CMOS technologies
Bazzi, H. (2020) · theses.fr (ABES) · doi:10.70675/78f0c866z7434z4bd7z84b7zb84b151fd845
In-memory computing based on CMOS-RRAM hybrid architectures
Esmanhotto, E. (2022) · theses.fr (ABES) · doi:10.70675/1cf79608z3789z4025z8efcz817e8621bdd9
Fang, Z. (2013) · doi:10.32657/10356/54664
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Metadata
- Authors
- Yu, S., Gao, B., Fang, Z., Yu, H., Kang, J., & Wong, H.-S. P.
- Affiliations
- Stanford SystemX Alliance (US)
- Peking University (CN)
- Agency for Science, Technology and Research (SG)
- Southern University of Science and Technology (CN)
- Venue
- 2012 International Electron Devices Meeting
- Publisher
- IEEE
- Published
- 2012-12
- Pages
- 10.4.1-10.4.4
- ISSN
- 0163-1918 (ISSN-L)
- OpenAlex
- W1987760418
- Cited by
- 213
- References
- 13
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 4 · 2023: 12 · 2022: 13 · 2021: 23 · 2020: 24 · 2019: 21 · 2018: 29 · 2017: 21 · 2016: 19 · 2015: 15 · 2014: 13 · 2013: 10
- FWCI
- 15.52
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Technical digest - International Electron Devices Meeting/Technical digest (publishedVersion)
- Funders
- National Science Foundation
- 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
- resistive random access memory, metal oxide memory, energy-efficient neuromorphic hardware
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