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Optoelectronic memr...
Optoelectronic memristor model for optical synaptic circuit of spiking neural networks
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- Xu, Jiawei (författare)
- KTH,Elektronik och inbyggda system,Guangdong Institute of Intelligence Science and Technology, Zhuhai, China
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- Zheng, Yi (författare)
- Fudan University, School of Information Science and Technology, Shanghai, China
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- Sheng, Chenxu (författare)
- Fudan University, School of Information Science and Technology, Shanghai, China
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- Cai, Yichen (författare)
- Fudan University, School of Information Science and Technology, Shanghai, China
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- Stathis, Dimitrios (författare)
- KTH,Elektronik och inbyggda system
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- Shen, Ruisi (författare)
- Fudan University, School of Information Science and Technology, Shanghai, China
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- Zheng, Li Rong (författare)
- 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 (författare)
- Fudan University, School of Information Science and Technology, Shanghai, China
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- Hu, Laigui (författare)
- Fudan University, School of Information Science and Technology, Shanghai, China
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Hemani, Ahmed, 1961- (författare)
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- Engelska.
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Ingår i: 21st IEEE Interregional NEWCAS Conference, NEWCAS 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- 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)
Nyckelord
- memristor model
- optical synapse
- Optoelectric memristor
- STDP learning rule
- trace dynamics
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
- Av författaren/redakt...
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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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visa fler...
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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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visa färre...
- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Data och informa ...
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och Bioinformatik
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Elektroteknik oc ...
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och Kommunikationssy ...
- Artiklar i publikationen
- 21st IEEE Interr ...
- Av lärosätet
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Kungliga Tekniska Högskolan