LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies
This addresses workload distribution challenges in edge-cloud AI systems, offering a novel design-time approach for improved efficiency.
The paper tackled the problem of designing DNN architectures for edge-cloud systems by proposing LENS, a multi-objective NAS method that incorporates wireless communication parameters, resulting in improvements of 76.47% in energy and 75% in latency over traditional solutions.
Edge-Cloud hierarchical systems employing intelligence through Deep Neural Networks (DNNs) endure the dilemma of workload distribution within them. Previous solutions proposed to distribute workloads at runtime according to the state of the surroundings, like the wireless conditions. However, such conditions are usually overlooked at design time. This paper addresses this issue for DNN architectural design by presenting a novel methodology, LENS, which administers multi-objective Neural Architecture Search (NAS) for two-tiered systems, where the performance objectives are refashioned to consider the wireless communication parameters. From our experimental search space, we demonstrate that LENS improves upon the traditional solution's Pareto set by 76.47% and 75% with respect to the energy and latency metrics, respectively.