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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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Miyashita, D., Kousai, S., Suzuki, T., & Deguchi, J. (2016) · 2016 IEEE Asian Solid-State Circuits Conference (A-SSCC) · doi:10.1109/asscc.2016.7844126 · Cited by 25
Novel Method Enabling Forward and Backward Propagations in NAND Flash Memory for On-Chip Learning
Lee, S.-T., Yeom, G., Yoo, H., Kim, H., Lim, S., Bae, J.-H., Park, B.-G., & Lee, J.-H. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3081610 · Cited by 17
Du, Y., Jing, L., Fang, H., Chen, H., Cai, Y., Wang, R., Zhang, J., & Ji, Z. (2020) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2020.3002736 · Cited by 24
Intrinsic variation effect in memristive neural network with weight quantization
Park, J., Song, M. S., Youn, S., Kim, T., Kim, S., Hong, K., & Kim, H. (2022) · Nanotechnology · doi:10.1088/1361-6528/ac7651 · Cited by 23
An Efficient In-Memory Computing Architecture for Image Enhancement in AI Applications
Bettayeb, M., Zayer, F., Abunahla, H., Gianini, G., & Mohammad, B. S. (2022) · IEEE Access · doi:10.1109/access.2022.3171799 · Cited by 23
Wang, J., Li, Y., Liang, R., Zhang, Y., Mao, W., Yang, Y., & Ren, T. (2017) · IEEE Electron Device Letters · doi:10.1109/led.2017.2745482 · Cited by 23
Woo, S. Y., Choi, K.-B., Kim, J., Kang, W.-M., Kim, C.-H., Seo, Y.-T., Bae, J., Park, B., & Lee, J. (2019) · Solid-State Electronics · doi:10.1016/j.sse.2019.107741 · Cited by 23
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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Tosson, A. M. S., Yu, S., Anis, M. H., & Wei, L. (2017) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2017.2734819 · Cited by 23
Self-citationTowards Synaptic Behavior of Nanoscale ReRAM Devices for Neuromorphic Computing Applications
Beckmann, K., Olin-Ammentorp, W., Chakma, G., Amer, S., Rose, G. S., Hobbs, C., Nostrand, J. V., Rodgers, M. P., & Cady, N. C. (2020) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3381859 · Cited by 21
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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