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.

翻译：利用深神经网络(DNNs)智能的边缘等级系统承受着它们内部工作量分配的两难困境。 先前曾提出过根据周围环境状况(如无线条件)在运行时分配工作量的办法, 但这些条件通常在设计时被忽略。 本文提出一个新的方法( LENS)来讨论DNN的建筑设计问题。 LENS管理双层系统的多目标神经结构搜索(NAS),该方法的性能目标被调整为考虑无线通信参数。 我们从实验搜索空间可以看出, LENS改进了传统解决方案的Pareto, 能源和延绳度测量标准分别设定为76.47%和75%。