Cited by 213Advanced Memory and Neural ComputingNeural Networks and Reservoir ComputingFerroelectric and Negative Capacitance Devices

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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

Yu, Shimeng, Gao, Bin, Fang, Zheng, Yu, Hongyu, Kang, Jinfeng, Wong, H.-S. Philip·2012·2012 International Electron Devices Meeting

DOI 10.1109/iedm.2012.6479018

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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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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)
ORCID: Yu, Shimeng
Gao, Bin
Fang, Zheng
Yu, Hongyu
Kang, Jinfeng
Wong, H.-S. Philip
Venue: 2012 International Electron Devices Meeting
Publisher: IEEE
Published: 2012-12
Pages: 10.4.1-10.4.4
ISSN: 0163-1918 (ISSN-L)
DOI: 10.1109/iedm.2012.6479018
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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A neuromorphic visual system using RRAM synaptic devices with Sub-pJ energy and tolerance to variability: Experimental characterization and large-scale modeling

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