  Model compression plays a vital role in the practical deployment of deep
neural networks (DNNs), and evolutionary multi-objective (EMO) pruning is an
essential tool in balancing the compression rate and performance of the DNNs.
However, due to its population-based nature, EMO pruning suffers from the
complex optimization space and the resource-intensive structure verification
process, especially in complex networks. To this end, a multi-objective complex
network pruning framework based on divide-and-conquer and global performance
impairment ranking (EMO-DIR) is proposed in this paper. Firstly, a
divide-and-conquer EMO network pruning method is proposed, which decomposes the
complex task of EMO pruning on the entire network into easier sub-tasks on
multiple sub-networks. On the one hand, this decomposition narrows the pruning
optimization space and decreases the optimization difficulty; on the other
hand, the smaller network structure converges faster, so the proposed algorithm
consumes lower computational resources. Secondly, a sub-network training method
based on cross-network constraints is designed, which could bridge independent
EMO pruning sub-tasks, allowing them to collaborate better and improving the
overall performance of the pruned network. Finally, a multiple sub-networks
joint pruning method based on EMO is proposed. This method combines the Pareto
Fronts from EMO pruning results on multiple sub-networks through global
performance impairment ranking to design a joint pruning scheme. The rich
experiments on CIFAR-10/100 and ImageNet-100/1k are conducted. The proposed
algorithm achieves a comparable performance with the state-of-the-art pruning
methods.
