Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications.
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ABSTRACT:
SUBMITTER: Sorbaro M
PROVIDER: S-EPMC7339957 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications.
Sorbaro Martino M Liu Qian Q Bortone Massimo M Sheik Sadique S
Frontiers in neuroscience 20200630
In the last few years, spiking neural networks (SNNs) have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice of performance. We demonstrate first that quantization-aware training of CNNs leads to better accuracy in SNNs. One of the benefits of converting CNNs to spiking CNNs is to leverage the sparse computation of SNNs and consequently perform equivalen ...[more]
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