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    On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks

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    Type
    Article
    Authors
    Payvand, Melika
    Fouda, Mohammed E.
    Kurdahi, Fadi
    Eltawil, Ahmed cc
    Neftci, Emre O.
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/666121
    
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    Abstract
    Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity. Although SNNs can be scalably implemented using neuromorphic VLSI, an architecture that can learn using gradient-descent in situ is still missing. In this paper, we propose a local, gradient-based, error-triggered learning algorithm with online ternary weight updates. The proposed algorithm enables online training of multi-layer SNNs with memristive neuromorphic hardware showing a small loss in the performance compared with the state-of-the-art. We also propose a hardware architecture based on memristive crossbar arrays to perform the required vector-matrix multiplications. The necessary peripheral circuitry including presynaptic, post-synaptic and write circuits required for online training, have been designed in the subthreshold regime for power saving with a standard 180nm CMOS process.
    Citation
    Payvand, M., Fouda, M. E., Kurdahi, F., Eltawil, A. M., & Neftci, E. O. (2020). On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 1–1. doi:10.1109/jetcas.2020.3040248
    Publisher
    IEEE
    Journal
    IEEE Journal on Emerging and Selected Topics in Circuits and Systems
    DOI
    10.1109/JETCAS.2020.3040248
    arXiv
    2011.10852
    Additional Links
    https://ieeexplore.ieee.org/document/9268117/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9268117
    http://arxiv.org/pdf/2011.10852
    ae974a485f413a2113503eed53cd6c53
    10.1109/JETCAS.2020.3040248
    Scopus Count
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    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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