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Genetic Algorithm-Based Online-Partitioning BranchyNet for Accelerating Edge Inference.

Jun Na1, Handuo Zhang2, Jiaxin Lian2

  • 1Software College, Northeastern University, Shenyang 110169, China.

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|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a genetic algorithm (GA) for distributing deep neural networks (DNNs) with early-exit branches across edge devices. The novel approach optimizes task partitioning for faster execution and lower energy consumption in intelligent edge applications.

Keywords:
BranchyNetDNN partitioningdistributed DNN inferencinggenetic algorithm

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Area of Science:

  • Artificial Intelligence
  • Computer Engineering
  • Distributed Systems

Background:

  • Edge intelligent applications require efficient deployment of deep neural networks (DNNs) on resource-constrained devices.
  • Existing methods often focus on partitioning specific branches of multi-branch DNNs, limiting flexibility.
  • Distributing the entire DNN, including all branches, across multiple edge devices presents a significant challenge.

Purpose of the Study:

  • To propose a novel online partitioning approach for BranchyNet, a DNN with multiple early-exit branches, for edge intelligent applications.
  • To optimize the distribution of the entire BranchyNet across heterogeneous edge devices.
  • To reduce execution time and energy consumption while enabling dynamic deployment.

Main Methods:

  • Development of a genetic algorithm (GA)-based online partitioning strategy that considers the entire BranchyNet and all its branches.
  • Introduction of a weighted average-based calculation for estimating the total execution time of a partitioned BranchyNet.
  • Implementation of a two-layer chromosome GA to differentiate between partitioning and deployment stages during optimization.

Main Results:

  • The proposed GA-based approach significantly reduces execution time compared to existing methods.
  • Lower device-average energy costs were achieved through optimized task distribution.
  • The algorithm demonstrates a reduced time to obtain an optimal deployment plan, enabling online adaptation.

Conclusions:

  • The developed online partitioning method effectively distributes BranchyNets across edge devices, optimizing performance.
  • The GA-based approach provides a dynamic and efficient solution for deploying intelligent applications at the edge.
  • This strategy facilitates real-time adaptation to changing requirements in distributed intelligent systems.