  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.
