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SlimEdge: Lightweight Distributed DNN Deployment on Constrained Hardware
arXiv:2512.22136v1 Announce Type: new
Abstract: Deep distributed networks (DNNs) have become central to modern computer vision, yet their deployment on resource-constrained edge devices remains hindered by substantial parameter counts and computational demands. Here, we present an approach to the efficient deployment of distributed DNNs that jointly respects hardware limitations and preserves task performance. Our method integrates a structured model pruning with a multi-objective optimization to tailor network capacity to heterogeneous device constraints. We demonstrate this framework using Multi-View Convolutional Neural Network (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, respectively. Experimental results show that the resulting models satisfy user-specified bounds on accuracy and memory footprint while reducing inference latency by factors ranging from 1.2x to 5.0x across diverse hardware platforms. These findings suggest that performance-aware, view-adaptive compression provides a viable pathway for deploying complex vision models in distributed edge environments.
Abstract: Deep distributed networks (DNNs) have become central to modern computer vision, yet their deployment on resource-constrained edge devices remains hindered by substantial parameter counts and computational demands. Here, we present an approach to the efficient deployment of distributed DNNs that jointly respects hardware limitations and preserves task performance. Our method integrates a structured model pruning with a multi-objective optimization to tailor network capacity to heterogeneous device constraints. We demonstrate this framework using Multi-View Convolutional Neural Network (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, respectively. Experimental results show that the resulting models satisfy user-specified bounds on accuracy and memory footprint while reducing inference latency by factors ranging from 1.2x to 5.0x across diverse hardware platforms. These findings suggest that performance-aware, view-adaptive compression provides a viable pathway for deploying complex vision models in distributed edge environments.
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