Joint Optimization of Model Partitioning and Resource Allocation for Multi-Exit DNNs in Edge-Device Collaboration

Abstract

The increasing complexity and computational demands of deep neural networks (DNNs) pose significant challenges and deployment on resource-constrained devices due to substantial latency and considerable energy consumption. Multi-exit DNNs have emerged as a promising solution, enabling simple tasks to exit early at intermediate network layers, thereby reducing inference latency and improving efficiency. However, relying solely on the computational capacity of end devices is often insufficient to meet performance requirements. Edge computing, by offloading part of the model’s computation to edge servers, has become a key solution to address this issue. Despite the potential of multi-exit DNNs in edge computing environments, two major challenges remain: model partitioning and resource allocation on edge servers. Existing research often focuses on model partitioning strategies or resource allocation strategies in isolation, neglecting the mutual optimization between the two. Moreover, energy consumption, a critical performance metric, is frequently overlooked in the optimization process. To address these issues, this paper proposes a joint optimization framework for model partitioning and resource allocation, integrating multi-exit DNNs and incorporating a deep reinforcement learning (DRL)-based optimization algorithm. Experimental results demonstrate that the proposed method significantly reduces inference costs and enhances system performance.