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

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 c...

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Veröffentlicht in:2021 58th ACM/IEEE Design Automation Conference (DAC) S. 403 - 408
Hauptverfasser: Odema, Mohanad, Rashid, Nafiul, Demirel, Berken Utku, Faruque, Mohammad Abdullah Al
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 05.12.2021
Schlagworte:
Deep learning
Design automation
Design methodology
Hierarchical systems
Runtime
Search problems
Wireless communication
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Abstract 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.
AbstractList 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.
Author Faruque, Mohammad Abdullah Al
Demirel, Berken Utku
Rashid, Nafiul
Odema, Mohanad
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  givenname: Mohanad
  surname: Odema
  fullname: Odema, Mohanad
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  organization: University of California,Department of Electrical Engineering and Computer Science,Irvine,California,USA
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  givenname: Nafiul
  surname: Rashid
  fullname: Rashid, Nafiul
  email: nafiulr@uci.edu
  organization: University of California,Department of Electrical Engineering and Computer Science,Irvine,California,USA
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  givenname: Berken Utku
  surname: Demirel
  fullname: Demirel, Berken Utku
  email: bdemirel@uci.edu
  organization: University of California,Department of Electrical Engineering and Computer Science,Irvine,California,USA
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  givenname: Mohammad Abdullah Al
  surname: Faruque
  fullname: Faruque, Mohammad Abdullah Al
  email: alfaruqu@uci.edu
  organization: University of California,Department of Electrical Engineering and Computer Science,Irvine,California,USA
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Snippet Edge-Cloud hierarchical systems employing intelligence through Deep Neural Networks (DNNs) endure the dilemma of workload distribution within them. Previous...
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SubjectTerms Deep learning
Design automation
Design methodology
Hierarchical systems
Runtime
Search problems
Wireless communication
Title LENS: Layer Distribution Enabled Neural Architecture Search in Edge-Cloud Hierarchies
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