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Memristor-Based In-...
Memristor-Based In-Circuit Computation for Trace-Based STDP
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- Wang, Deyu (författare)
- Fudan Univ, Sch Informat Sci & Technol, State Key Lab ASIC & Syst, Shanghai, Peoples R China.
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- Xu, Jiawei (författare)
- Fudan Univ, Sch Informat Sci & Technol, State Key Lab ASIC & Syst, Shanghai, Peoples R China.
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- Li, Feng (författare)
- Fudan Univ, Sch Informat Sci & Technol, State Key Lab ASIC & Syst, Shanghai, Peoples R China.
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- Zhang, Lianhao (författare)
- Tech Univ Denmark, Dept Elect Engn, Lyngby, Denmark.
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- Wang, Yuning (författare)
- Univ Turku, Dept Future Technol, Turku, Finland.
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- Lansner, Anders, Professor, 1949- (författare)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
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- Hemani, Ahmed, 1961- (författare)
- KTH,Elektronik och inbyggda system
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- Zheng, Li-Rong (författare)
- Fudan Univ, Sch Informat Sci & Technol, State Key Lab ASIC & Syst, Shanghai, Peoples R China.
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- Zou, Zhuo (författare)
- Fudan Univ, Sch Informat Sci & Technol, State Key Lab ASIC & Syst, Shanghai, Peoples R China.
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Fudan Univ, Sch Informat Sci & Technol, State Key Lab ASIC & Syst, Shanghai, Peoples R China Tech Univ Denmark, Dept Elect Engn, Lyngby, Denmark. (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- Engelska.
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Ingår i: 2022 Ieee International Conference On Artificial Intelligence Circuits And Systems (Aicas 2022). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1-4
- 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
- Recently, memristors have been widely used to implement Spiking Neural Networks (SNNs), which is promising in edge computing scenarios. However, most memristor-based SNN implementations adopt simplified spike-timing-dependent plasticity (STDP) for the online learning process. It is challenging for memristor-based implementations to support the trace-based STDP learning rules that have been widely used in neuromorphic applications. This paper proposed a versatile memristor-based architecture to implement the synaptic-level trace-based STDP learning rules. Especially, the similarity between synaptic trace dynamics and the memristor nonlinearity is explored and exploited to emulate the trace variables of trace-based STDP. As two typical trace-based STDP learning rules, the pairwise STDP and the triplet STDP, are simulated on two typical nonlinear bipolar memristor devices. The simulation results show that the behavior of physical memristor devices can be well estimated (below 6% in terms of the relative root-mean-square error), and the memristor-based in-circuit computation for trace-based STDP learning rules can achieve a high correlation coefficient over 98%.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Nyckelord
- Memristor
- trace
- spike-timing-dependent plasticity (STDP)
- online learning
- spiking neural network (SNN)
- neuromorphic computation
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
- Av författaren/redakt...
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Wang, Deyu
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Xu, Jiawei
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Li, Feng
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Zhang, Lianhao
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Wang, Yuning
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Lansner, Anders, ...
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visa fler...
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Hemani, Ahmed, 1 ...
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Zheng, Li-Rong
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Zou, Zhuo
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visa färre...
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