Partitioned Collaborative Inference for On-Device Models via Evolutionary Reinforcement Learning

Le résumé fourni par la source

The growing demand for intelligent mobile applications has made the deployment and operation of Deep Neural Networks (DNNs) on mobile Edge Devices (EDs) increasingly essential. However, the limited computational resources of EDs often result in significant energy consumption and compromised inference quality. To address these challenges, we propose a Partitioned Collaborative Inference (PCI) system that reduces on-device model inference costs by distributing the inference process across multiple EDs and MEC servers. To dynamically model the relationships between computing nodes, inference tasks, and resources, we employ Graph Neural Networks to construct the current state representation of the system. Furthermore, we develop a Cross-Entropy Method (CEM) based Evolutionary Reinforcement Learning algorithm, which leverages negative temporal difference (TD) error as a population fitness metric to generate elite individuals. The elite produces high-quality samples to improve learning efficiency, thereby obtaining optimal partitioned collaborative inference decisions and resource allocation in highly dynamic and complex search spaces. Extensive simulations demonstrate that the proposed approach significantly outperforms existing methods and benchmark schemes, achieving a 57. 5% increase in the inference task completion rate and a 65.7% reduction in system costs.

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