Pruning Filters for Efficient ConvNets

31 Aug 2016  ·  Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, Hans Peter Graf ·

The success of CNNs in various applications is accompanied by a significant increase in the computation and parameter storage costs. Recent efforts toward reducing these overheads involve pruning and compressing the weights of various layers without hurting original accuracy. However, magnitude-based pruning of weights reduces a significant number of parameters from the fully connected layers and may not adequately reduce the computation costs in the convolutional layers due to irregular sparsity in the pruned networks. We present an acceleration method for CNNs, where we prune filters from CNNs that are identified as having a small effect on the output accuracy. By removing whole filters in the network together with their connecting feature maps, the computation costs are reduced significantly. In contrast to pruning weights, this approach does not result in sparse connectivity patterns. Hence, it does not need the support of sparse convolution libraries and can work with existing efficient BLAS libraries for dense matrix multiplications. We show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34% and ResNet-110 by up to 38% on CIFAR10 while regaining close to the original accuracy by retraining the networks.

PDF Abstract

Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Network Pruning ImageNet ResNet50-2.3 GFLOPs Accuracy 78.79 # 1
GFLOPs 2.335 # 5
MParams 14.811 # 1
Network Pruning ImageNet ResNet50-1.5 GFLOPs Accuracy 78.07 # 2
GFLOPs 1.635 # 2
MParams 10.511 # 2
Network Pruning ImageNet ResNet50-1G FLOPs Accuracy 76.376 # 7
GFLOPs 1.075 # 1
MParams 6.954 # 3

Methods