HGA-DP: Optimal Partitioning of Multimodal DNNs Enabling Real-Time Image Inference for AR-Assisted Communication Maintenance on Cloud-Edge-End Systems

In the field of communication maintenance, Augmented Reality (AR) applications are critical for enhancing operational safety and efficiency. However, deploying the required multimodal models on resource-constrained terminal devices is challenging, as traditional cloud or on-device strategies fail to balance low latency and energy consumption. This paper proposes a Cloud-Edge-End collaborative inference framework tailored to multimodal model deployment. A subgraph partitioning strategy is introduced to systematically decompose complex multimodal models into functionally independent sub-units. Subsequently, a fine-grained performance estimation model is employed to accurately characterize both computation and communication costs across heterogeneous devices. And, a joint optimization problem is formulated to minimize end-to-end inference latency and terminal energy consumption. To solve this problem efficiently, a Hybrid Genetic Algorithm for DNN Partitioning (HGA-DP) evolved over 100 generations is designed, incorporating constraint-aware repair mechanisms and local neighborhood search to navigate the exponential search space of possible deployment combinations. Experimental results on a simulated three-tier collaborative computing platform demonstrate that, compared to traditional full on-device deployment, the proposed method reduces end-to-end inference latency by 70–80% and terminal energy consumption by 81.1%, achieving a 4.86× improvement in overall fitness score. Against the latency-optimized DADS heuristic, HGA-DP achieves 41.3% lower latency while reducing energy by 59.9%. Compared to the All-Cloud strategy, our approach delivers 71.5% latency reduction with only marginal additional terminal energy cost. This framework provides an adaptive and effective solution for real-time multimodal inference in resource-constrained scenarios, laying a foundation for intelligent, resource-aware deployment.