Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications

In the last few years, spiking neural networks 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...

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Bibliographic Details
Published in:Frontiers in neuroscience 2020-06, Vol.14, p.662-662
Main Authors: Sorbaro, Martino, Liu, Qian, Bortone, Massimo, Sheik, Sadique
Format: Article
Language:English
Subjects:
convolutional networks
Energy consumption
Firing pattern
loss function
Neural networks
neuromorphic computing
Neurons
Neuroscience
spiking networks
synaptic operations
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Summary:In the last few years, spiking neural networks 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 efficient optimization strategy to train spiking networks at lower energy consumption, while maintaining similar accuracy levels. We demonstrate results on the MNIST-DVS and CIFAR-10 datasets.
ISSN:1662-453X
1662-4548
1662-453X
DOI:10.3389/fnins.2020.00662