Deep-learning-based applications bring impressive results to graph machine learning and are widely used in fields such as autonomous driving and language translations. Nevertheless, the tremendous capacity of convolutional neural networks makes it difficult for them to be implemented on resource-constrained devices. Channel pruning provides a promising solution to compress networks by removing a redundant calculation. Existing pruning methods measure the importance of each filter and discard the less important ones until reaching a fixed compression target. However, the static approach limits the pruning effect. Thus, we propose a dynamic channel-pruning method that dynamically identifies and removes less important filters based on a redundancy analysis of its feature maps. Experimental results show that 77.10% of floating-point operations per second (FLOPs) and 91.72% of the parameters are reduced on VGG16BN with only a 0.54% accuracy drop. Furthermore, the compressed models were implemented on the field-programmable gate array (FPGA) and a significant speed-up was observed.

The heavy workload of current deep learning architectures significantly impedes the application of deep learning, especially on resource-constrained devices. Pruning has provided a promising solution to compressing the bloated deep learning models by removing the redundancies of the networks. However, existing pruning methods mainly focus on compressing the superfluous channels without considering layer-level redundancies, which results in the channel-pruned models still suffering from serious redundancies. To mitigate this problem, we propose an effective compression algorithm for deep learning models that uses both the channel-level and layer-level compression techniques to optimize the enormous deep learning models. In detail, the channels are dynamically pruned first, and then the model is further optimized by fusing the redundant layers. Only a minor performance loss results. The experimental results show that the computations of ResNet-110 are reduced by 80.05%, yet the accuracy is only decreased by 0.72%. Forty-eight convolutional layers could be discarded from ResNet-110 with no loss of performance, which fully demonstrates the efficiency of the proposal.