Collaborative deep neural network inference via distributed partitioning and resource optimization in vehicular edge computing

In the Internet of Vehicles (IoV), collaborative inference based on Deep Neural Networks (DNNs) faces considerable challenges due to the dynamic and heterogeneous service demands of multiple vehicular devices supported by edge servers. These challenges give rise to a complex joint optimization problem that simultaneously determines the optimal DNN partition points and allocates computing resources from servers to individual devices. This problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) model, involving both discrete partitioning decisions and continuous resource allocation variables, which makes it computationally demanding in large-scale and dynamic environments. To address this issue, we propose a fully distributed algorithm that achieves rapid convergence to feasible solutions. Furthermore, to accelerate the distributed iterative solving process, an adaptive strategy is designed to dynamically adjust partition points and resource allocations according to the real-time server load. For Directed Acyclic Graph (DAG)-structured DNNs, a novel structural transformation technique is developed to convert them into equivalent chain representations, enabling unified optimization across heterogeneous DNN topologies. Theoretical analysis validates the feasibility and near-optimality of the proposed approach under standard consensus assumptions, while empirical results demonstrate stable performance in dynamic vehicular environments. Extensive experiments conducted in realistic vehicular scenarios demonstrate the superior effectiveness and scalability of the proposed method in reducing inference latency and system overhead.