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Optoelectronic memr...
Optoelectronic memristor model for optical synaptic circuit of spiking neural networks
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- Xu, Jiawei (author)
- KTH,Elektronik och inbyggda system,Guangdong Institute of Intelligence Science and Technology, Zhuhai, China
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- Zheng, Yi (author)
- Fudan University, School of Information Science and Technology, Shanghai, China
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- Sheng, Chenxu (author)
- Fudan University, School of Information Science and Technology, Shanghai, China
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- Cai, Yichen (author)
- Fudan University, School of Information Science and Technology, Shanghai, China
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- Stathis, Dimitrios (author)
- KTH,Elektronik och inbyggda system
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- Shen, Ruisi (author)
- Fudan University, School of Information Science and Technology, Shanghai, China
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- Zheng, Li Rong (author)
- Guangdong Institute of Intelligence Science and Technology, Zhuhai, China; Fudan University, School of Information Science and Technology, Shanghai, China
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- Zou, Zhuo (author)
- Fudan University, School of Information Science and Technology, Shanghai, China
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- Hu, Laigui (author)
- Fudan University, School of Information Science and Technology, Shanghai, China
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Hemani, Ahmed, 1961- (author)
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- English.
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In: 21st IEEE Interregional NEWCAS Conference, NEWCAS 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- Optoelectronic memristors are suitable candidates for hardware implementation of optical synapses in spiking neural networks (SNNs), thanks to their electrical and optical characteristics. To study the feasibility of memristor-based optical synapses in SNNs, a behavior model for optoelectronic memristors is proposed in this paper, including electrical programming modeling and photocurrent read modeling. Based on the model, the behavior of a molecular ferroelectric (MF)/semiconductor interfacial memristor is simulated. This paper also proposes an optical synaptic circuit for trace-based spike-timing-dependent plasticity (STDP) learning rule. The electrical characteristics of the memristor are explored and exploited to emulate the trace in the pairwise nearest-neighbor STDP, while the optical characteristics are utilized for non-destructive readout and weight calculation. Synaptic-level simulation results show a 99.96% correlation coefficient (CC) and a 1.91% relative root mean square error (RRMSE) in the weight approximate computation. Extending the simulation to the network level, the optoelectronic memristor-based unsupervised STDP learning system can achieve a 92.07± 0.64% accuracy on the MNIST benchmark.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
Keyword
- memristor model
- optical synapse
- Optoelectric memristor
- STDP learning rule
- trace dynamics
Publication and Content Type
- ref (subject category)
- kon (subject category)
To the university's database
- By the author/editor
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Xu, Jiawei
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Zheng, Yi
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Sheng, Chenxu
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Cai, Yichen
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Stathis, Dimitri ...
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Shen, Ruisi
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show more...
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Zheng, Li Rong
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Zou, Zhuo
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Hu, Laigui
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Hemani, Ahmed, 1 ...
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show less...
- About the subject
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Computer and Inf ...
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and Bioinformatics
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- ENGINEERING AND TECHNOLOGY
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ENGINEERING AND ...
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and Electrical Engin ...
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and Communication Sy ...
- Articles in the publication
- 21st IEEE Interr ...
- By the university
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Royal Institute of Technology