# LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies

arXiv: https://arxiv.org/abs/2107.09309 · HTML: https://mlsanity.com/paper/2107.09309/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 20 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly](https://mlsanity.com/paper/1903.06694/) (2019)
- [SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers](https://mlsanity.com/paper/1905.12107/) (2019)
- [EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions](https://mlsanity.com/paper/2005.02563/) (2020)
- [Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator](https://mlsanity.com/paper/2002.05022/) (2020)
- [TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networks](https://mlsanity.com/paper/1811.12065/) (2018)

## Built on by (most cited first, 5 of 5)

- [SAGE: A Split-Architecture Methodology for Efficient End-to-End Autonomous Vehicle Control](https://mlsanity.com/paper/2107.10895/) (2021)
- [Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems](https://mlsanity.com/paper/2404.05605/) (2024)
- [Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systems](https://mlsanity.com/paper/2207.08865/) (2022)
- [Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey](https://mlsanity.com/paper/2208.10498/) (2022)
- [GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search](https://mlsanity.com/paper/2512.11856/) (2025)

## Abstract (arXiv)

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.