Neuromorphic speech systems using advanced ReRAM-based synapse
Abstract
We demonstrate an advanced ReRAM based analog artificial synapse for neuromorphic systems. Nitrogen doped TiN/PCMO based artificial synapse is proposed to improve the performance and reliability of the neuromorphic systems by using simple identical spikes. For the first time, we develop fully unsupervised learning with proposed analog synapses which is illustrated with the help of auditory and electroencephalography (EEG) applications.
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The proposed work exhibits higher training accuracy compared to [30], [32], [34], and [35].
“Additionally, our proposed work exhibits higher training accuracy compared to [30], [32], [34], and [35].”
From Framework for In-Memory Computing Based on Memristor and Memcapacitor for On-Chip Training (2023) · cites this paper as [34]
The proposed VMM accelerator achieves a smaller footprint compared to [32], [33], [34], and [35].
“whereas our proposed VMM accelerator achieves a smaller footprint compared to [32], [33], [34], and [35].”
From Framework for In-Memory Computing Based on Memristor and Memcapacitor for On-Chip Training (2023) · cites this paper as [34]
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“The size of the crossbar in [30] and [32] is large, leading to impractical area consumption, whereas our proposed VMM accelerator achieves a smaller footprint compared to [32], [33], [34], and [35].”
BackgroundResultSelf-citation
- Authors
- Park, S., Sheri, A., Kim, J., Noh, J., Jang, J., Jeon, M., Lee, B., Lee, B. R., Lee, B. H., & Hwang, H.
- Affiliations
- Gwangju Institute of Science and Technology (KR)
- Pohang University of Science and Technology (KR)
- Venue
- 2013 IEEE International Electron Devices Meeting
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 25.6.1-25.6.4
- ISSN
- 0163-1918 (ISSN-L)
- OpenAlex
- W2026145098
- Cited by
- 201
- References
- 0
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 13 · 2023: 22 · 2022: 16 · 2021: 27 · 2020: 21 · 2019: 15 · 2018: 25 · 2017: 17 · 2016: 14 · 2015: 11 · 2014: 2
- FWCI
- 10.96
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Technical digest - International Electron Devices Meeting/Technical digest (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Keywords
- neuromorphic systems, resistive random access memory, artificial synapses, analog synapses, unsupervised learning, auditory signal processing, EEG signal processing
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