MIXP: Efficient Deep Neural Networks Pruning for Further FLOPs Compression via Neuron Bond

Neuron networks pruning is effective in compressing pre-trained CNNs for their deployment on low-end edge devices. However, few works have focused on reducing the computational cost of pruning and inference. We find that existing pruning methods usually remove parameters without fine-grained impact analysis, making it hard to achieve an optimal solution. This work develops a novel mixture pruning mechanism, MIXP, which can effectively reduce the computational cost of CNNs while maintaining a high weight compression ratio and model accuracy. We propose to remove neuron bond that can effectively reduce convolution computations and weight size in CNNs. We also design an influence factor to analyze the importance of neuron bonds and weights in a fine-grained way so that MIXP could achieve precise pruning with few retraining iterations. Experiments with MNIST, CIFAR-10, and ImageNet datasets demonstrate that MIXP could achieve significantly fewer FLOPs and retraining iterations on four widely-used CNNs than existing pruning methods.