Balanced Sparsity for Efficient DNN Inference on GPU - Yao, Zhuliang; Cao, Shijie; Xiao, Wencong; Zhang, Chen; Nie, Lanshun (AAAI Conference on Artificial Intelligence (AAAI), 2019)
In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardware to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardware by adopting coarse-grained sparsity to prune or regularize consecutive weights for efﬁcient computation. But this method often sacriﬁces model accuracy. In this paper, we propose a novel ﬁne-grained sparsity approach, Balanced Sparsity, to achieve high model accuracy with commercial hardware efﬁciently. Our approach adapts to high parallelism property of GPU, showing incredible potential for sparsity in the widely deployment of deep learning services. Experiment results show that Balanced Sparsity achieves up to 3.1x practical speedup for model inference on GPU, while retains the same high model accuracy as ﬁne-grained sparsity.