Classification Performances due to Asymmetric Nonlinear Weight Updates in Analog Artificial Synapse-Based Hardware Neural Networks

Yeon Pyo; Sahn Nahm; Jichai Jeong

doi:10.1109/BCI53720.2022.9734968

Classification Performances due to Asymmetric Nonlinear Weight Updates in Analog Artificial Synapse-Based Hardware Neural Networks

Yeon Pyo, Sahn Nahm, Jichai Jeong

Department of Materials Science and Engineering

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

Abstract

Artificial synapses are fundamental for neuromorphic computing to overcome the bottleneck of the von Neumann system. In particular, a memristor synapse-based neuromorphic system has been known as an optimal device for effectively implementing a hardware neural network. Here, we propose the memristor synapse which shows potentiation and depression process like biological brain mechanisms and investigate the effects of varying the device parameters of nonlinearity and asymmetry on the classification accuracy. We find that the virtual devices with a nonlinearity of less than 10 can be obtained the classification accuracy up to 80%. Our approach demonstrates a practical neuromorphic system based on virtual device on simulation and measured device on experiment and verifies the feasibility of the hardware neural networks.

Original language	English
Title of host publication	10th International Winter Conference on Brain-Computer Interface, BCI 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665413374
DOIs	https://doi.org/10.1109/BCI53720.2022.9734968
Publication status	Published - 2022
Event	10th International Winter Conference on Brain-Computer Interface, BCI 2022 - Gangwon-do, Korea, Republic of Duration: 2022 Feb 21 → 2022 Feb 23

Publication series

Name	International Winter Conference on Brain-Computer Interface, BCI
Volume	2022-February
ISSN (Print)	2572-7672

Conference

Conference	10th International Winter Conference on Brain-Computer Interface, BCI 2022
Country/Territory	Korea, Republic of
City	Gangwon-do
Period	22/2/21 → 22/2/23

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Artificial synapse
Memristor
Neural network
Neuromorphic system

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Signal Processing

Access to Document

10.1109/BCI53720.2022.9734968

Cite this

Pyo, Y., Nahm, S., & Jeong, J. (2022). Classification Performances due to Asymmetric Nonlinear Weight Updates in Analog Artificial Synapse-Based Hardware Neural Networks. In 10th International Winter Conference on Brain-Computer Interface, BCI 2022 (International Winter Conference on Brain-Computer Interface, BCI; Vol. 2022-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BCI53720.2022.9734968

Classification Performances due to Asymmetric Nonlinear Weight Updates in Analog Artificial Synapse-Based Hardware Neural Networks. / Pyo, Yeon; Nahm, Sahn; Jeong, Jichai.
10th International Winter Conference on Brain-Computer Interface, BCI 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (International Winter Conference on Brain-Computer Interface, BCI; Vol. 2022-February).

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

Pyo, Y, Nahm, S & Jeong, J 2022, Classification Performances due to Asymmetric Nonlinear Weight Updates in Analog Artificial Synapse-Based Hardware Neural Networks. in 10th International Winter Conference on Brain-Computer Interface, BCI 2022. International Winter Conference on Brain-Computer Interface, BCI, vol. 2022-February, Institute of Electrical and Electronics Engineers Inc., 10th International Winter Conference on Brain-Computer Interface, BCI 2022, Gangwon-do, Korea, Republic of, 22/2/21. https://doi.org/10.1109/BCI53720.2022.9734968

Pyo Y, Nahm S, Jeong J. Classification Performances due to Asymmetric Nonlinear Weight Updates in Analog Artificial Synapse-Based Hardware Neural Networks. In 10th International Winter Conference on Brain-Computer Interface, BCI 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (International Winter Conference on Brain-Computer Interface, BCI). doi: 10.1109/BCI53720.2022.9734968

Pyo, Yeon ; Nahm, Sahn ; Jeong, Jichai. / Classification Performances due to Asymmetric Nonlinear Weight Updates in Analog Artificial Synapse-Based Hardware Neural Networks. 10th International Winter Conference on Brain-Computer Interface, BCI 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (International Winter Conference on Brain-Computer Interface, BCI).

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Classification Performances due to Asymmetric Nonlinear Weight Updates in Analog Artificial Synapse-Based Hardware Neural Networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this