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    Toward the Optimal Design and FPGA Implementation of Spiking Neural Networks

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    Manuscript (7).pdf
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    Accepted manuscript
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    Type
    Article
    Authors
    Guo, Wenzhe cc
    Yantir, Hasan Erdem cc
    Fouda, Mohamed E. cc
    Eltawil, Ahmed cc
    Salama, Khaled N. cc
    KAUST Department
    Electrical Engineering Program
    Electrical Engineering
    Physical Science and Engineering (PSE) Division
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2021
    Submitted Date
    2020-02-18
    Permanent link to this record
    http://hdl.handle.net/10754/667399
    
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    Abstract
    The performance of a biologically plausible spiking neural network (SNN) largely depends on the model parameters and neural dynamics. This article proposes a parameter optimization scheme for improving the performance of a biologically plausible SNN and a parallel on-field-programmable gate array (FPGA) online learning neuromorphic platform for the digital implementation based on two numerical methods, namely, the Euler and third-order Runge-Kutta (RK3) methods. The optimization scheme explores the impact of biological time constants on information transmission in the SNN and improves the convergence rate of the SNN on digit recognition with a suitable choice of the time constants. The parallel digital implementation leads to a significant speedup over software simulation on a general-purpose CPU. The parallel implementation with the Euler method enables around 180x (20x) training (inference) speedup over a Pytorch-based SNN simulation on CPU. Moreover, compared with previous work, our parallel implementation shows more than 300x (240x) improvement on speed and 180x (250x) reduction in energy consumption for training (inference). In addition, due to the high-order accuracy, the RK3 method is demonstrated to gain 2x training speedup over the Euler method, which makes it suitable for online training in real-time applications.
    Citation
    Guo, W., Yantir, H. E., Fouda, M. E., Eltawil, A. M., & Salama, K. N. (2021). Toward the Optimal Design and FPGA Implementation of Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 1–15. doi:10.1109/tnnls.2021.3055421
    Sponsors
    This work was supported by the King Abdullah University of Science and Technology (KAUST) AI Initiative, Saudi Arabia
    Publisher
    IEEE
    Journal
    IEEE Transactions on Neural Networks and Learning Systems
    DOI
    10.1109/TNNLS.2021.3055421
    Additional Links
    https://ieeexplore.ieee.org/document/9353400/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9353400
    ae974a485f413a2113503eed53cd6c53
    10.1109/TNNLS.2021.3055421
    Scopus Count
    Collections
    Articles; Physical Science and Engineering (PSE) Division; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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