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Papers/Sparse Spiking Gradient Descent

Sparse Spiking Gradient Descent

Nicolas Perez-Nieves, Dan F. M. Goodman

2021-05-18NeurIPS 2021 12Image ClassificationAudio Classification
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Abstract

There is an increasing interest in emulating Spiking Neural Networks (SNNs) on neuromorphic computing devices due to their low energy consumption. Recent advances have allowed training SNNs to a point where they start to compete with traditional Artificial Neural Networks (ANNs) in terms of accuracy, while at the same time being energy efficient when run on neuromorphic hardware. However, the process of training SNNs is still based on dense tensor operations originally developed for ANNs which do not leverage the spatiotemporally sparse nature of SNNs. We present here the first sparse SNN backpropagation algorithm which achieves the same or better accuracy as current state of the art methods while being significantly faster and more memory efficient. We show the effectiveness of our method on real datasets of varying complexity (Fashion-MNIST, Neuromophic-MNIST and Spiking Heidelberg Digits) achieving a speedup in the backward pass of up to 150x, and 85% more memory efficient, without losing accuracy.

Results

TaskDatasetMetricValueModel
Audio ClassificationSHDPercentage correct77.5Sparse Spiking Gradient Descent
Image ClassificationFashion-MNISTAccuracy86.7Sparse Spiking Gradient Descent (CNN)
Image ClassificationFashion-MNISTAccuracy82.7Sparse Spiking Gradient Descent (MLP)
Image ClassificationN-MNISTAccuracy92.7Sparse Spiking Gradient Descent
ClassificationSHDPercentage correct77.5Sparse Spiking Gradient Descent

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