Large-Scale Multiobjective Model Pruning for Intelligent Transport Systems

Deep neural networks can provide environment sensing and decision support for vehicles in 6G intelligent autonomous transportation systems; however, the high computational cost associated with complex tasks limits the deployment of models on edge devices. To address this issue, this paper introduces a large-scale multiobjective filter pruning method, which stratifies the population by Angle Penalty Distance (APD) and selects the individuals to be updated. Meanwhile, the global search capability of the algorithm is enhanced by combining sampling update strategies and quantum behavioral update, in different situations. Moreover, a dynamic optimization tuning strategy is proposed to regulate the balance between exploration and exploitation within the algorithm. In the depth estimation task, the model is pruned using three objectives: root mean square error (RMSE), number of parameters, and FLOPs. Experimental results indicate that the pruned model obtains the minimum RMSE and the maximum compression ratio of the number of parameters and FLOPs, and can be effectively deployed in sensing-computing integrated chip and system for intelligent transportation systems.