When deep learning models on GPU can be accelerated by taking advantage of unstructured sparsity

• URL: http://arxiv.org/abs/2011.06295v2
• Date: Sat, 17 Apr 2021 11:26:46 GMT
• Title: When deep learning models on GPU can be accelerated by taking advantage of unstructured sparsity
• Authors: Marcin Pietro\'n, Dominik \.Zurek
• Abstract summary: This paper is focused on the improvement the efficiency of the sparse convolutional neural networks (CNNs) layers on graphic processing units ( GPU) The modern CNN models need megabytes of coefficients and needed millions MAC operations to perform convolution. We show when is worth using a direct sparse operation to speed-up the calculation of the convolution layers.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper is focused on the improvement the efficiency of the sparse convolutional neural networks (CNNs) layers on graphic processing units (GPU). The Nvidia deep neural network (cuDnn) library provides the most effective implementation of deep learning (DL) algorithms for GPUs. GPUs are one of the most efficient and commonly used accelerators for deep learning computations. The modern CNN models need megabytes of coefficients and needed millions MAC operations to perform convolution. One of the most common techniques for compressing CNN models is weight pruning. There are two main types of pruning: structural (based on removing whole weight channels) and non-structural (removing individual weights). The first enables much easier acceleration, but with this type it is difficult to achieve a sparsity level and accuracy as high as that obtained with the second type. Non-structural pruning with retraining can generate a matrix-weight up to $\sim90\%$ or more of sparsity in some deep CNN models. This work shows when is worth using a direct sparse operation to speed-up the calculation of the convolution layers. The VGG-16, CNN-non-static and 1x1 layers from ResNet models were used as a benchmarks. In addition, we present the impact of using reduced precision on time efficiency.

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