Distributed deep neural networks (DNNs) have become central to modern computer vision, yet their deployment on resource-constrained edge devices remains hindered by substantial parameter counts, computational demands, and the probability of device failure. Here, we present an approach to the efficient deployment of distributed DNNs that jointly respect hardware limitations, preserve task performance, and remain robust to partial system failures. Our method integrates structured model pruning with a multi-objective optimization framework to tailor network capacity for heterogeneous device constraints, while explicitly accounting for device availability and failure probability during deployment. We demonstrate this framework using Multi-View Convolutional Neural Networks (MVCNN), a state-of-the-art architecture for 3D object recognition, by quantifying the contribution of individual views to classification accuracy and allocating pruning budgets accordingly. Experimental results show that the resulting models satisfy user-specified bounds on accuracy and memory footprint, even under multiple simultaneous device failures. The inference time is reduced by factors up to 4.7x across diverse simulated device configurations. These findings suggest that performance-aware, view-adaptive, and failure-resilient compression provides a viable pathway for deploying complex vision models in distributed edge environments.

翻译：分布式深度神经网络已成为现代计算机视觉的核心，但其在资源受限边缘设备上的部署仍受参数量庞大、计算需求高以及设备故障概率等因素的制约。本文提出一种分布式深度神经网络的高效部署方法，该方法在协同兼顾硬件限制的同时保持任务性能，并对局部系统故障具备鲁棒性。本方法将结构化模型剪枝与多目标优化框架相结合，根据异构设备约束定制网络容量，并在部署过程中显式考虑设备可用性与故障概率。我们采用三维物体识别领域的先进架构——多视角卷积神经网络作为验证框架，通过量化单视角对分类精度的贡献度并相应分配剪枝预算进行实证研究。实验结果表明，即使在多设备并发故障场景下，所得模型仍能满足用户设定的精度与内存占用边界约束。在多种模拟设备配置中，推理时间最高可降低至原时间的4.7倍。这些发现表明，性能感知、视角自适应且具备故障恢复能力的压缩技术为复杂视觉模型在分布式边缘环境中的部署提供了可行路径。