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Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications.

ABSTRACT: 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 equivalent computation at a lower energy consumption. Here we propose an optimization strategy to train efficient spiking networks with lower energy consumption, while maintaining similar accuracy levels. We demonstrate results on the MNIST-DVS and CIFAR-10 datasets.

SUBMITTER: Sorbaro M

PROVIDER: S-EPMC7339957 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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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]

PMID: 32694978

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