SlimEdge: Lightweight Distributed DNN Deployment on Constrained Hardware (arxiv.org)

A recent study introduces SlimEdge, a novel approach for deploying deep distributed networks (DNNs) on resource-constrained edge devices, addressing the challenges posed by high parameter counts and computational demands typical of these models. By integrating structured model pruning with multi-objective optimization, SlimEdge tailors network capacity to the specific limitations of heterogeneous hardware. This framework is exemplified through the use of the Multi-View Convolutional Neural Network (MVCNN), advancing 3D object recognition by efficiently determining the optimal contribution of individual views to maintain classification accuracy. The significance of SlimEdge resonates deeply within the AI/ML community, as it enables the deployment of complex computer vision models in distributed edge environments without sacrificing performance. Experimental results highlight a remarkable enhancement in efficiency, achieving inference latency reductions between 1.2x to 5.0x while adhering to prescribed accuracy and memory constraints. This performance-aware, view-adaptive compression technique not only paves the way for more agile AI applications in edge computing but also sets a precedent for future innovations aimed at optimizing DNN deployment in similarly constrained contexts.