SlimEdge: Performance and Device Aware Distributed DNN Deployment on Resource-Constrained Edge Hardware
Mahadev Sunil Kumar, Arnab Raha, Debayan Das, Gopakumar G, Rounak Chatterjee, Amitava Mukherjee

TL;DR
This paper introduces SlimEdge, a framework for deploying distributed DNNs on resource-limited edge devices that optimizes model pruning considering hardware constraints, task accuracy, and device failures, demonstrated on MVCNN for 3D recognition.
Contribution
It presents a novel multi-objective optimization approach integrating structured pruning with failure resilience for distributed DNN deployment on edge hardware.
Findings
Inference time reduced by up to 4.7x
Models meet accuracy and memory bounds under failures
Framework effectively allocates pruning based on view importance
Abstract
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · Human Pose and Action Recognition · Advanced Memory and Neural Computing
