Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing

Nan Li*, Alexandros Iosifidis, Qi Zhang

*Corresponding author for this work

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing network. To avoid inference accuracy loss in inference task partitioning, we propose receptive field-based segmentation (RFS). To reduce the computation time and communication overhead, we propose a novel collaborative edge computing using fused-layer parallelization to partition a CNN model into multiple blocks of convolutional layers. In this scheme, the collaborative edge servers (ESs) only need to exchange small fraction of the sub-outputs after computing each fused block. In addition, to find the optimal solution of partitioning a CNN model into multiple blocks, we use dynamic programming, named as dynamic programming for fused-layer parallelization (DPFP). The experimental results show that DPFP can accelerate inference of VGG-16 up to 73% compared with the pre-trained model, which outperforms the existing work MoDNN in all tested scenarios. Moreover, we evaluate the service reliability of DPFP under time-variant channel, which shows that DPFP is an effective solution to ensure high service reliability with strict service deadline.

Original languageEnglish
Title of host publicationICC 2022 - IEEE International Conference on Communications
Number of pages6
Publication date2022
ISBN (Print)978-1-5386-8348-4
ISBN (Electronic)978-1-5386-8347-7
Publication statusPublished - 2022
EventIEEE International Conference on Communications - Coex, Seoul, Korea, Republic of
Duration: 16 May 202220 May 2022


ConferenceIEEE International Conference on Communications
Country/TerritoryKorea, Republic of
Internet address


  • Distributed CNNs
  • Edge computing
  • Receptive field
  • Service reliability
  • Time-critical IoT


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