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]
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Cites
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
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Neuromorphic electronic systems
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Short-term plasticity and long-term potentiation mimicked in single inorganic synapses
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Short-Term Memory to Long-Term Memory Transition in a Nanoscale Memristor
Chang, T., Jo, S.-H., & Lu, W. (2011) · ACS Nano · doi:10.1021/nn202983n · Cited by 1,058
Elements of Artificial Neural Networks
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Yu, S., Wu, Y., Jeyasingh, R., Kuzum, D., & Wong, H.-S. P. (2011) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2011.2147791 · Cited by 805
Self-citationOn 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
Seo, K., Kim, I., Jung, S., Jo, M., Park, S., Park, J., Shin, J., Biju, K. P., Kong, J., Lee, K., et al. (2011) · Nanotechnology · doi:10.1088/0957-4484/22/25/254023 · Cited by 286
A SPICE Compact Model of Metal Oxide Resistive Switching Memory With Variations
Guan, X., Yu, S., & Wong, H. ‐. P. (2012) · IEEE Electron Device Letters · doi:10.1109/led.2012.2210856 · Cited by 283
Self-citationSelf-organized computation with unreliable, memristive nanodevices
Snider, G. S. (2007) · Nanotechnology · doi:10.1088/0957-4484/18/36/365202 · Cited by 264
Neuromorphic Silicon Neurons and Large-Scale Neural Networks: Challenges and Opportunities
Poon, C., & Zhou, K. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00108 · Cited by 237
Fang, Z., Yu, H. Y., Li, X., Singh, N., Lo, G. Q., & Kwong, D. L. (2011) · IEEE Electron Device Letters · doi:10.1109/led.2011.2109033 · Cited by 167
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Research works printed but not linked: 3
- yu, 2011, IEDM, 413
- kuzum, 2011, IEDM, 693
- suri, 2011, IEDM, 79
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Synaptic electronics: materials, devices and applications
Kuzum, D., Yu, S., & Wong, H. ‐. P. (2013) · Nanotechnology · doi:10.1088/0957-4484/24/38/382001 · Cited by 1,257
Self-citationNeuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
Self-citationNeuromorphic computing using non-volatile memory
Burr, G. W., Shelby, R. M., Sebastian, A., Kim, S., Kim, S., Sidler, S., Virwani, K. R., Ishii, M., Narayanan, P., Fumarola, A., et al. (2016) · Advances in Physics X · doi:10.1080/23746149.2016.1259585 · Cited by 1,196
Zahoor, F., Zulkifli, T. Z. A., & Khanday, F. A. (2020) · Nanoscale Research Letters · doi:10.1186/s11671-020-03299-9 · Cited by 985
Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices
Querlioz, D., Bichler, O., Dollfus, P., & Gamrat, C. (2013) · IEEE Transactions on Nanotechnology · doi:10.1109/tnano.2013.2250995 · Cited by 434
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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