Dual-angle parallel pruning method for deep neural networks under requirement for lightweight edge resources

ZHANG Yunxiang; GAO Shengpu

doi:10.7688/j.issn.1000-1646.2025.02.15

Abstract：[Objective]In the application process of deep neural networks,their huge computing requirements and storage overhead have become bottlenecks that restrict their widespread application on edge devices.Edge devices are limited by deficient computing resources and storage space,which makes it particularly difficult for them to efficiently run complex deep neural network models.Therefore,how to reduce the complexity and computational load of deep neural networks while maintaining model accuracy to meet the requirements of edge devices for lightweight edge resources has become an important research topic at present.To improve the performance of deep neural networks in edge devices,an optimization method for deep neural networks was proposed which combines the ant colony algorithm and dual-angle parallel pruning.[Methods]The structural characteristics of deep neural networks were analyzed,and a deep neural network model with multiple hidden layers was constructed.The ant colony algorithm was utilized to search for approximate optimal solutions in complex spaces by simulating the pheromone transmission mechanism in the process of ants foraging.Similar nodes in the hidden layers of the constructed model were clustered to identify highly similar neuron nodes and group them into the same category,which reduced the scale and complexity of the network.On this basis,dual-angle parallel pruning processing was further carried out on redundant nodes and free nodes after clustering.On the one hand,from the perspective of the sparsity of the weight matrix,nodes with small weights were pruned to reduce computational overhead.On the other hand,from the perspective of node contribution,the contribution of each node to the overall output result was evaluated,and nodes with small contribution were pruned.[Results]The experimental results show that compared to the original model without pruning,the deep neural network pruned using the proposed method has a higher data volume of 120 MB,an average network complexity of 88.32％,and scalability of 99％while maintaining a high accuracy within the same computation time.This means that under limited resource conditions,deep neural networks can run more efficiently and better adapt to the needs of edge devices with the help of the proposed method.The experimental results not only validate the effectiveness of the proposed method but also provide new ideas for the deployment and application of deep neural networks on edge devices.[Conclusion]The proposed method applies ant colony algorithm to the pruning process of deep neural networks,achieving effective clustering of similar nodes in the hidden layers and providing accurate targets for subsequent pruning.At the same time,the dual-angle parallel pruning strategy further improves the efficiency and effectiveness of pruning,ensuring the balance between accuracy and scalability of the pruned model.The proposed method can not only promote the widespread application of deep neural networks on edge devices but also provide useful reference and guidance for complex network optimization problems in other fields.