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Modeling Cycle-to-C...
Modeling Cycle-to-Cycle Variation in Memristors for In-Situ Unsupervised Trace-STDP Learning
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- Xu, Jiawei (författare)
- KTH,Elektronik och inbyggda system,KGuangdong Inst Intelligence Sci & Technol, Zhuhai 519115, Guangdong, Peoples R China.
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- Zheng, Yi (författare)
- Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China.
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- Li, Feng (författare)
- Guangdong Inst Intelligence Sci & Technol, Zhuhai 519115, Guangdong, Peoples R China.
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- Stathis, Dimitrios (författare)
- KTH,Elektronik och inbyggda system
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- Shen, Ruisi (författare)
- Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China.
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- Chu, Haoming (författare)
- Guangdong Inst Intelligence Sci & Technol, Zhuhai 519115, Guangdong, Peoples R China.
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- Lansner, Anders, Professor, 1949- (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
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- Zheng, Li-Rong (författare)
- Guangdong Inst Intelligence Sci & Technol, Zhuhai 519115, Guangdong, Peoples R China.
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- Zou, Zhuo (författare)
- Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China.
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- Hemani, Ahmed, 1961- (författare)
- KTH,Elektronik och inbyggda system
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2024
- 2024
- Engelska.
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Ingår i: IEEE Transactions on Circuits and Systems - II - Express Briefs. - : Institute of Electrical and Electronics Engineers (IEEE). - 1549-7747 .- 1558-3791. ; 71:2, s. 627-631
- 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
- Evaluating the computational accuracy of Spiking Neural Network (SNN) implemented as in-situ learning on large-scale memristor crossbars remains a challenge due to the lack of a versatile model for the variations in non-ideal memristors. This brief proposes a novel behavioral variation model along with a four-stage pipeline for physical memristors. The proposed variation model combines both absolute and relative variations. Therefore, it can better characterize different memristor cycle-to-cycle (C2C) variations in practice. The proposed variation model has been used to simulate the behavior of two physical memristors. Adopting the non-ideal memristor model, the trace-based spiking-timing dependent plasticity (STDP) unsupervised in-memristor learning system is simulated. Although the synaptic-level weight simulation shows a performance degradation of 7.99% and 4.07% increase in the relative root mean square error (RRMSE), the network-level simulation results show no accuracy loss on the MNIST benchmark. Furthermore, the impacts of absolute and relative C2C variations on network performance are simulated and analyzed through two sets of univariate experiments.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Nyckelord
- Memristors
- Correlation
- Integrated circuit modeling
- Behavioral sciences
- Mathematical models
- Computational modeling
- Task analysis
- Memristor
- non-ideality
- variation model
- trace-based STDP
- in-situ unsupervised learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Xu, Jiawei
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Zheng, Yi
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Li, Feng
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Stathis, Dimitri ...
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Shen, Ruisi
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Chu, Haoming
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Lansner, Anders, ...
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Zheng, Li-Rong
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Zou, Zhuo
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Hemani, Ahmed, 1 ...
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- 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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IEEE Transaction ...
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Kungliga Tekniska Högskolan