Aarhus Universitets segl

Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing

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This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing. To ensure inference accuracy in inference task partitioning, we consider the receptive-field when performing segment-based partitioning. To maximize the parallelization between the communication and computing processes, thereby minimizing the total inference time of an inference task, we design a novel task collaboration scheme in which the overlapping zone of the sub-tasks on secondary edge servers (ESs) is executed on the host ES, named as HALP. We further extend HALP to the scenario of multiple tasks. Experimental results show that HALP can accelerate CNN inference in VGG-16 by 1.7-2.0x for a single task and 1.7-1.8x for 4 tasks per batch on GTX 1080TI and JETSON AGX Xavier, which outperforms the state-of-the-art work MoDNN. Moreover, we evaluate the service reliability under time-variant channel, which shows that HALP is an effective solution to ensure high service reliability with strict service deadline.

TitelICC 2022 - IEEE International Conference on Communications
Antal sider6
Udgivelsesårmaj 2022
ISBN (Elektronisk)978-1-5386-8347-7
StatusUdgivet - maj 2022
BegivenhedIEEE International Conference on Communications - Coex, Seoul, Sydkorea
Varighed: 16 maj 202220 maj 2022


KonferenceIEEE International Conference on Communications

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