Dynamic DNN Scheduling on Heterogeneous Multiprocessors for Industrial Defect Detection

In industrial defect detection, the massive computational overhead of deep neural networks (DNNs) makes it difficult to satisfy the hard real-time constraints of edge devices. Furthermore, existing methods are typically limited to single-dimensional scaling and lack joint optimization for heterogeneous multi-core architectures. To address this issue, we propose a joint optimization and scheduling framework for dynamic DNNs oriented toward heterogeneous multiprocessor systems. At the network layer, we design a dynamic DNN architecture that supports the three-dimensional collaborative scaling of depth, width, and resolution, alongside an incremental training strategy. This significantly enhances both the model's inference accuracy and training efficiency. At the scheduling layer, a priority dual-queue mechanism based on image block weighting is introduced. The heterogeneous task scheduling is formulated as a multi-level generalized assignment problem, and a heuristic algorithm based on linear relaxation and bidirectional dynamic adjustment (LRBDA) is proposed to solve the heterogeneous scheduling problem. Experimental results demonstrate that the proposed hybrid-scaling architecture significantly outperforms single-dimensional scaling strategies and existing multi-branch dynamic networks in balancing accuracy and computational cost, reducing the computational overhead by up to 23.54% under the same detection accuracy. Furthermore, compared to the mixed-criticality EKG scheduling algorithm (MC-EKG) algorithm, the LRBDA algorithm overcomes the performance bottleneck associated with long periods, significantly improving the average system accuracy and schedulability range under stringent computational constraints.