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Poster
Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
Xiaohan Ding · guiguang ding · Xiangxin Zhou · Yuchen Guo · Jungong Han · Ji Liu

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #102
in Deep Learning -- Efficient Inference Methods »

Deep Neural Network (DNN) is powerful but computationally expensive and memory intensive, thus impeding its practical usage on resource-constrained front-end devices. DNN pruning is an approach for deep model compression, which aims at eliminating some parameters with tolerable performance degradation. In this paper, we propose a novel momentum-SGD-based optimization method to reduce the network complexity by on-the-fly pruning. Concretely, given a global compression ratio, we categorize all the parameters into two parts at each training iteration which are updated using different rules. In this way, we gradually zero out the redundant parameters, as we update them using only the ordinary weight decay but no gradients derived from the objective function. As a departure from prior methods that require heavy human works to tune the layer-wise sparsity ratios, prune by solving complicated non-differentiable problems or finetune the model after pruning, our method is characterized by 1) global compression that automatically finds the appropriate per-layer sparsity ratios; 2) end-to-end training; 3) no need for a time-consuming re-training process after pruning; and 4) superior capability to find better winning tickets which have won the initialization lottery.

Keywords: Deep Learning · Efficient Inference Methods · Deep Learning
[ Paper]  [ Poster] 

Author Information

Xiaohan Ding (Tsinghua University)
guiguang ding (Tsinghua University, China)
Xiangxin Zhou (Tsinghua University)
Yuchen Guo (Tsinghua University)
Jungong Han (Lancaster University)
Ji Liu (Kwai Inc.)

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