mlsanityData to 7 Oct 2026

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

arXiv 2107.09309 ↗First posted 20 Jul 2021cs.LG cs.DC cs.NEcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 8 of its 18 references that are in the map. First posted on arXiv 20 Jul 2021.

Builds on

8 of 18 references.

References · 8

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  3. 2016Designing Neural Network Architectures using Reinforcement Learningcited by 277
  4. 2019Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonflycited by 66
  5. 2019SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollerscited by 52
  6. 2020EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutionscited by 35
  7. 2020Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Acceleratorcited by 30
  8. 2018TEA-DNN: the Quest for Time-Energy-Accuracy Co-optimized Deep Neural Networkscited by 3

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Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 1; 2025: 1.

5 papers.

  1. 2021SAGE: A Split-Architecture Methodology for Efficient End-to-End Autonomous Vehicle Controlcited by 8
  2. 2024Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systemscited by 5
  3. 2022Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systemscited by 3
  4. 2022Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Surveycited by 1
  5. 2025GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Searchcited by 0