Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity

Suhyun Bang; Min Hye Oh; Min Hwi Kim; Tae Hyeon Kim; Dong Keun Lee; Yeon Joon Choi; Chae Soo Kim; Kyungho Hong; Seongjae Cho; Sungjun Kim; Byung Gook Park

doi:10.1109/ICEIC49074.2020.9051210

Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity

Suhyun Bang, Min Hye Oh, Min Hwi Kim, Tae Hyeon Kim, Dong Keun Lee, Yeon Joon Choi, Chae Soo Kim, Kyungho Hong, Seongjae Cho, Sungjun Kim, Byung Gook Park

Electronic and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In this work, we have developed a spiking neural network (SNN) using gradual resistive-switching random-access memory (RRAM) synaptic device. The fabricated RRAM devices demonstrated the characteristics of gradually changing conductance with voltage pulses under both positive and negative polarities, which is suitable for imitating the potentiation and depression functions of a biological synapse by an electron device. Featuring the gradual switching characteristics, spike-rate-dependent plasticity (SRDP) inspired by Bienenstock, Cooper, and Munro (BCM) learning rule was confirmed and modeled for synaptic modification in the SNN. Then, the supervised learning of MNIST patterns was performed on the simulated SNNs, by which it has been validated that the proposed resistive-switching synaptic device and SRDP synaptic modification rule can adjust weights accurately in cooperation without necessitating the conventional calculation-based learning scheme in the artificial neural networks (ANNs), such as error backpropagation.

Original language	English
Title of host publication	2020 International Conference on Electronics, Information, and Communication, ICEIC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728162898
DOIs	https://doi.org/10.1109/ICEIC49074.2020.9051210
State	Published - Jan 2020
Event	2020 International Conference on Electronics, Information, and Communication, ICEIC 2020 - Barcelona, Spain Duration: 19 Jan 2020 → 22 Jan 2020

Publication series

Name	2020 International Conference on Electronics, Information, and Communication, ICEIC 2020

Conference

Conference	2020 International Conference on Electronics, Information, and Communication, ICEIC 2020
Country/Territory	Spain
City	Barcelona
Period	19/01/20 → 22/01/20

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation (NRF) Grant funded by the Ministry of Science and ICT of Korea (MSIT) (2018R1A2A1A05023517 and 2016M3A7B4910348) and by the Brain Korea 21 Plus Program in 2019.

Publisher Copyright:
© 2020 IEEE.

Keywords

Resistive-switching random-access memory
Spike-rate-dependent plasticity
Spiking neural network
Synaptic device

Access to Document

10.1109/ICEIC49074.2020.9051210

Cite this

Bang, S., Oh, M. H., Kim, M. H., Kim, T. H., Lee, D. K., Choi, Y. J., Kim, C. S., Hong, K., Cho, S., Kim, S., & Park, B. G. (2020). Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity. In 2020 International Conference on Electronics, Information, and Communication, ICEIC 2020 Article 9051210 (2020 International Conference on Electronics, Information, and Communication, ICEIC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEIC49074.2020.9051210

Bang, Suhyun ; Oh, Min Hye ; Kim, Min Hwi et al. / Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity. 2020 International Conference on Electronics, Information, and Communication, ICEIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 International Conference on Electronics, Information, and Communication, ICEIC 2020).

@inproceedings{f9cc296045b747958d062fda571123f5,

title = "Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity",

abstract = "In this work, we have developed a spiking neural network (SNN) using gradual resistive-switching random-access memory (RRAM) synaptic device. The fabricated RRAM devices demonstrated the characteristics of gradually changing conductance with voltage pulses under both positive and negative polarities, which is suitable for imitating the potentiation and depression functions of a biological synapse by an electron device. Featuring the gradual switching characteristics, spike-rate-dependent plasticity (SRDP) inspired by Bienenstock, Cooper, and Munro (BCM) learning rule was confirmed and modeled for synaptic modification in the SNN. Then, the supervised learning of MNIST patterns was performed on the simulated SNNs, by which it has been validated that the proposed resistive-switching synaptic device and SRDP synaptic modification rule can adjust weights accurately in cooperation without necessitating the conventional calculation-based learning scheme in the artificial neural networks (ANNs), such as error backpropagation.",

keywords = "Resistive-switching random-access memory, Spike-rate-dependent plasticity, Spiking neural network, Synaptic device",

author = "Suhyun Bang and Oh, {Min Hye} and Kim, {Min Hwi} and Kim, {Tae Hyeon} and Lee, {Dong Keun} and Choi, {Yeon Joon} and Kim, {Chae Soo} and Kyungho Hong and Seongjae Cho and Sungjun Kim and Park, {Byung Gook}",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by the National Research Foundation (NRF) Grant funded by the Ministry of Science and ICT of Korea (MSIT) (2018R1A2A1A05023517 and 2016M3A7B4910348) and by the Brain Korea 21 Plus Program in 2019. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Conference on Electronics, Information, and Communication, ICEIC 2020 ; Conference date: 19-01-2020 Through 22-01-2020",

year = "2020",

month = jan,

doi = "10.1109/ICEIC49074.2020.9051210",

language = "English",

series = "2020 International Conference on Electronics, Information, and Communication, ICEIC 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Bang, S, Oh, MH, Kim, MH, Kim, TH, Lee, DK, Choi, YJ, Kim, CS, Hong, K, Cho, S, Kim, S & Park, BG 2020, Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity. in 2020 International Conference on Electronics, Information, and Communication, ICEIC 2020., 9051210, 2020 International Conference on Electronics, Information, and Communication, ICEIC 2020, Institute of Electrical and Electronics Engineers Inc., 2020 International Conference on Electronics, Information, and Communication, ICEIC 2020, Barcelona, Spain, 19/01/20. https://doi.org/10.1109/ICEIC49074.2020.9051210

Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity. / Bang, Suhyun; Oh, Min Hye; Kim, Min Hwi et al.
2020 International Conference on Electronics, Information, and Communication, ICEIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9051210 (2020 International Conference on Electronics, Information, and Communication, ICEIC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity

AU - Bang, Suhyun

AU - Oh, Min Hye

AU - Kim, Min Hwi

AU - Kim, Tae Hyeon

AU - Lee, Dong Keun

AU - Choi, Yeon Joon

AU - Kim, Chae Soo

AU - Hong, Kyungho

AU - Cho, Seongjae

AU - Kim, Sungjun

AU - Park, Byung Gook

N1 - Funding Information: ACKNOWLEDGMENT This work was supported by the National Research Foundation (NRF) Grant funded by the Ministry of Science and ICT of Korea (MSIT) (2018R1A2A1A05023517 and 2016M3A7B4910348) and by the Brain Korea 21 Plus Program in 2019. Publisher Copyright: © 2020 IEEE.

PY - 2020/1

Y1 - 2020/1

N2 - In this work, we have developed a spiking neural network (SNN) using gradual resistive-switching random-access memory (RRAM) synaptic device. The fabricated RRAM devices demonstrated the characteristics of gradually changing conductance with voltage pulses under both positive and negative polarities, which is suitable for imitating the potentiation and depression functions of a biological synapse by an electron device. Featuring the gradual switching characteristics, spike-rate-dependent plasticity (SRDP) inspired by Bienenstock, Cooper, and Munro (BCM) learning rule was confirmed and modeled for synaptic modification in the SNN. Then, the supervised learning of MNIST patterns was performed on the simulated SNNs, by which it has been validated that the proposed resistive-switching synaptic device and SRDP synaptic modification rule can adjust weights accurately in cooperation without necessitating the conventional calculation-based learning scheme in the artificial neural networks (ANNs), such as error backpropagation.

AB - In this work, we have developed a spiking neural network (SNN) using gradual resistive-switching random-access memory (RRAM) synaptic device. The fabricated RRAM devices demonstrated the characteristics of gradually changing conductance with voltage pulses under both positive and negative polarities, which is suitable for imitating the potentiation and depression functions of a biological synapse by an electron device. Featuring the gradual switching characteristics, spike-rate-dependent plasticity (SRDP) inspired by Bienenstock, Cooper, and Munro (BCM) learning rule was confirmed and modeled for synaptic modification in the SNN. Then, the supervised learning of MNIST patterns was performed on the simulated SNNs, by which it has been validated that the proposed resistive-switching synaptic device and SRDP synaptic modification rule can adjust weights accurately in cooperation without necessitating the conventional calculation-based learning scheme in the artificial neural networks (ANNs), such as error backpropagation.

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M3 - Conference contribution

AN - SCOPUS:85083508567

T3 - 2020 International Conference on Electronics, Information, and Communication, ICEIC 2020

BT - 2020 International Conference on Electronics, Information, and Communication, ICEIC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Bang S, Oh MH, Kim MH, Kim TH, Lee DK, Choi YJ et al. Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity. In 2020 International Conference on Electronics, Information, and Communication, ICEIC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9051210. (2020 International Conference on Electronics, Information, and Communication, ICEIC 2020). doi: 10.1109/ICEIC49074.2020.9051210

Validation of spiking neural networks using resistive-switching synaptic device with spike-rate-dependent plasticity

Abstract

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Bibliographical note

Keywords

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