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Related Experiment Video

Updated: Oct 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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BBNet: A Novel Convolutional Neural Network Structure in Edge-Cloud Collaborative Inference.

Hongbo Zhou1,2, Weiwei Zhang1,2, Chengwei Wang1

  • 1College of Engineering, Huaqiao University, Quanzhou 362021, China.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces BBNet, a novel deep neural network structure designed to accelerate edge-cloud collaborative inference. BBNet reduces latency by pruning channels and compressing feature maps, improving efficiency especially under poor network conditions.

Keywords:
cloud computingcollaborative intelligencedeep learningfeature compressionmodel compression

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Edge-cloud collaborative inference offers reduced latency for deep neural networks (DNNs) by partitioning computations.
  • Increased intermediate data size in DNNs can lead to higher communication latency, negating benefits of collaborative inference.
  • Optimizing data transmission between edge and cloud is crucial for efficient collaborative DNN inference.

Purpose of the Study:

  • To propose a novel deep neural network structure, BBNet, for accelerated edge-cloud collaborative inference.
  • To address the challenge of increased communication latency due to large intermediate data sizes in collaborative DNNs.
  • To enhance the efficiency of DNN inference by optimizing both computation and communication.

Main Methods:

  • Developed BBNet, a convolutional neural network structure incorporating channel pruning to reduce computations and parameters.
  • Implemented feature map compression at the network split point to minimize transmitted data size.
  • Evaluated BBNet's performance on NVIDIA Nano and server, comparing it against the original network structure.

Main Results:

  • BBNet achieved significant reductions in FLOPs (up to 5.67×) and parameters (up to 11.57×) compared to the original network.
  • The feature compression layer demonstrated a high bit-compression rate, reaching up to 512× in optimal scenarios.
  • BBNet showed more pronounced inference delay improvements under poor network conditions (e.g., 20 kb/s upload bandwidth).

Conclusions:

  • BBNet effectively accelerates edge-cloud collaborative inference through channel pruning and feature map compression.
  • The proposed structure significantly reduces computational load and data transmission size, enhancing overall efficiency.
  • BBNet demonstrates superior performance, particularly in environments with limited bandwidth, offering a practical solution for low-latency DNN inference.