ReRAM-based synaptic device for neuromorphic computing

Jang, Jun-Woo; Park, Sangsu; Jeong, Yoon-Ha; Hwang, Hyunsang

doi:10.1109/iscas.2014.6865320

Cited by 67Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeural Networks and Applications

ReRAM-based synaptic device for neuromorphic computing

Jang, Jun-Woo, Park, Sangsu, Jeong, Yoon-Ha, Hwang, Hyunsang·2014·2014 IEEE International Symposium on Circuits and Systems (ISCAS)

DOI 10.1109/iscas.2014.6865320

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Abstract

To compete with nonvolatile FLASH memory technology, we need to develop stackable, cross-point ReRAM device. Although various materials have been reported, it is difficult to meet device criteria such as high speed operation, low power switching, switching uniformity, endurance, long-term retention and selection device for cross-point array. We have investigated various ReRAM devices to meet the nonvolatile memory requirements. We developed wafer scale PCMO-ReRAM devices which exhibit excellent switching uniformity and analog memory behavior. Using PCMO-ReRAM, we can implement analog artificial synapse characteristics for neuromorphic systems. We demonstrated the optimum ReRAM synapse characteristics which are suitable for the neuromorphic application. We compared the time series prediction and face recognition function with varying potentiation and depression characteristics of ReRAM synapse.

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In many materials, such as PCM, ReRAM, CBRAM, or ECRAM, conductances are effectively continuous physical quantities, and stored weights are not quantized.
“In many materials, such as phase-change memory (PCM)22,23, resistive random-access memory (ReRAM)24,25, conductive bridge RAM (CBRAM)26,27, or electro-chemical random-access memory (ECRAM)28,29, these conductances are effectively continuous physical quantities, and stored weights are not quantized.”
From Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators (2023) · cites this paper as [25]

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Authors: Jang, J.-W., Park, S., Jeong, Y.-H., & Hwang, H.
Affiliations: Pohang University of Science and Technology (KR)
Gwangju Institute of Science and Technology (KR)
ORCID: Hwang, Hyunsang
Venue: 2014 IEEE International Symposium on Circuits and Systems (ISCAS)
Publisher: IEEE
Published: 2014-06
Pages: 1054-1057
ISSN: 0271-4302 (ISSN-L)
DOI: 10.1109/iscas.2014.6865320
OpenAlex: W2070090854
Cited by: 67
References: 10
Citations by year: 2026: 6 · 2025: 7 · 2024: 4 · 2023: 11 · 2022: 9 · 2021: 3 · 2020: 4 · 2019: 5 · 2018: 7 · 2017: 5 · 2016: 3 · 2015: 3
FWCI: 4.46
Type: conference paper
Language: en
Open access: closed
Locations: IEEE International Symposium on Circuits and Systems proceedings (publishedVersion)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords: resistive random access memory, neuromorphic computing, artificial synapses, cross-point memory arrays, non-volatile memory, switching uniformity, synaptic potentiation and depression, time series forecasting, face recognition

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ReRAM-based synaptic device for neuromorphic computing

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