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An RRAM-based Analog Neuron Design for the Weighted Spiking Neural network

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Authors

Lee, Chaeun; Kim, Jaehyun; Choi, Kiyoung

Issue Date
2019-10
Publisher
IEEE
Citation
2019 INTERNATIONAL SOC DESIGN CONFERENCE (ISOCC), pp.259-260
Abstract
Spiking neural networks (SNNs) are promising because they have the ability to represent signal strength information with a simple sequence of spikes having the same height. In this paper, we propose an RRAM-based analog neuron circuit for the weighted spiking neural network which is energy-efficient and hardware-friendly. We have designed the neuron circuit to show that the weighted spiking neural network can be implemented in analog and works properly.
ISSN
2163-9612
URI
https://hdl.handle.net/10371/186405
DOI
https://doi.org/10.1109/ISOCC47750.2019.9078507
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