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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Many studies have reported that only two discrete states are demonstrated during potentiation, whereas depression demonstrates multiple states.
“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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Xu, C., Niu, D., Yu, S., & Xie, Y. (2014) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/aspdac.2014.6742992 · Cited by 27 · see citing works
Self-citationOxide-based analog synapse: Physical modeling, experimental characterization, and optimization
Gao, B., Wu, H., Kang, J., Yu, H., & Qian, H. (2016) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm.2016.7838367 · Cited by 27 · see citing works
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Well-Posed Models of Memristive DevicesPreprint
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Tuning the conductivity of resistive switching devices for electronic synapses
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A ferroelectric FET based power-efficient architecture for data-intensive computing
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Investigation of $I-V$ Linearity in TaOx-Based RRAM Devices for Neuromorphic Applications
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- 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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