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Resprop : reused sparsified backpropagation

University of British Columbia

The success of Convolutional Neural Networks (CNNs) in various applications is accompanied by a significant increase in computation and training time. In this work, we focus on accelerating training by observing that about 90% of gradients are reusable during training. Leveraging this observation, we propose a new algorithm, Reuse-Sparse-Backprop (ReSprop), as a method to sparsify gradient vectors during CNN training. ReSprop maintains state-of-the-art accuracy on CIFAR-10, CIFAR-100, and ImageNet datasets with less than 1.1% accuracy loss while enabling a reduction in back-propagation computations by a factor of 10x resulting in a 2.7x overall speedup in training. As the computation reduction introduced by ReSprop is accomplished by introducing fine-grained sparsity that reduces computation efficiency on GPUs, we introduce a generic sparse convolution neural network accelerator (GSCN), which is designed to accelerate sparse back-propagation convolutions. When combined with ReSprop, GSCN achieves 8.0x and 7.2x speedup in the backward pass on ResNet34 and VGG16 versus a GTX 1080 Ti GPU.

Text

2020-06-19

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/74770

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/