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

• URL: http://arxiv.org/abs/2207.11293v1
• Date: Fri, 22 Jul 2022 18:38:11 GMT
• Title: Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing
• Authors: Nan Li, Alexandros Iosifidis, Qi Zhang
• Abstract summary: We study inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing network. We propose a novel collaborative edge computing using fused-layer parallelization to partition a CNN model into multiple blocks of convolutional layers.
• Score: 93.67044879636093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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