M. M. Adnan, S. Sayyaparaju, S. D. Brown, M. S. A. Shawkat, C. D. Schuman and G. S. Rose

June, 2021

ACM Journal on Emerging Technologies in Computer Systems (JETC)

https://doi.org/10.1145/3451210

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Abstract

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. The implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.

Citation Information

Text

author     M. M. Adnan and S. Sayyaparaju and S. D. Brown and M. S. A. Shawkat and 
           C. D. Schuman and G. S. Rose
title      Design of a Robust Memristive Spiking Neuromorphic System with 
           Unsupervised Learning in Hardware
journal    ACM Journal on Emerging Technologies in Computer Systems (JETC)
volume     17
number     4
pages      1-26
year       2021
url        https://doi.org/10.1145/3451210
doi        10.1145/3451210

Bibtex

@ARTICLE{ksa:22:udr,
    author = "M. M. Adnan and S. Sayyaparaju and S. D. Brown and M. S. A. Shawkat and 
                C. D. Schuman and G. S. Rose",
    title = "Design of a Robust Memristive Spiking Neuromorphic System with 
                Unsupervised Learning in Hardware",
    journal = "ACM Journal on Emerging Technologies in Computer Systems (JETC)",
    volume = "17",
    number = "4",
    pages = "1-26",
    year = "2021",
    url = "https://doi.org/10.1145/3451210",
    doi = "10.1145/3451210"
}