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

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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.

OriginalsprogEngelsk
TitelICC 2022 - IEEE International Conference on Communications
Antal sider6
ForlagIEEE
Udgivelsesår2022
Sider4281-4286
ISBN (trykt)978-1-5386-8348-4
ISBN (Elektronisk)978-1-5386-8347-7
DOI
StatusUdgivet - 2022
BegivenhedIEEE International Conference on Communications - Coex, Seoul, Sydkorea
Varighed: 16 maj 202220 maj 2022
https://icc2022.ieee-icc.org/

Konference

KonferenceIEEE International Conference on Communications
LokationCoex
LandSydkorea
BySeoul
Periode16/05/202220/05/2022
Internetadresse

Bibliografisk note

Funding Information:
ACKNOWLEDGMENT This work is supported by Agile-IoT project (Grant No. 9131-00119B) granted by the Danish Council for Independent Research.

Publisher Copyright:
© 2022 IEEE.

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