Deploying deep neural networks (DNNs) on resource-constrained devices requires hybrid device–cloud inference in which end-to-end latency and data transmission must be optimized jointly. Existing Dynamic Split Computing (DSC) methods primarily target latency and overlook the computation–communication trade-off; as a result, varying bandwidth and batch size can lead to suboptimal operating points (e.g., excessive transmission for marginal latency gains). We propose Genetic Algorithm Multi-Objective (GA-MO), a genetic-algorithm framework that constructs Pareto fronts across diverse bandwidth scenarios and supports adaptive runtime selection. GA-MO jointly reduces inference latency and transmission load to satisfy application-specific performance requirements, including common real-time constraints. Beyond single-objective baselines, GA-MO is further shown to outperform a weighted-sum multi-objective approach, highlighting the effectiveness of evolutionary search in exploring the split-computing design space. Experiments on EfficientNet-B0 and VGG16 show that GA-MO outperforms DSC, achieving 55–60% lower latency and 52–62% bandwidth savings, with a 100% win rate across all tested conditions. These results indicate that GA-MO is an effective and scalable approach to multi-objective optimization for hybrid edge–cloud inference.
Nguyen et al. (Fri,) studied this question.