Extensive Assessment of Various Network Interruption Tools

Boqueo, Grace G.; Daya, Liriel Iris O.; Eugenio, Mark Erdan Ii S.; Lumagas, Akissa G.; Guzman, Froilan E. De

doi:10.1109/ccoms.2019.8821709

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Finally, the NVIDIA JETSON NANO is used to simulate the edge device, the PC simulates the edge server, and the model is used for collaborative reasoning. The WonderShaper [31] tool is used to control the bandwidth on the edge device and the edge server. The server, NANO, and PC are all loaded with the Ubuntu 18.04 system, and the Pytorch [32] environment is built.…”

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confidence: 99%

Accelerated Inference of Face Detection under Edge-Cloud Collaboration

Zhang

Zhou

et al. 2022

Applied Sciences

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Model compression makes it possible to deploy face detection models on devices with limited computing resources. Edge–cloud collaborative inference, as a new paradigm of neural network inference, can significantly reduce neural network inference latency. Inspired by these two techniques, this paper adopts a two-step acceleration strategy for the CenterNet model. Firstly, the model pruning method is used to prune the convolutional layer and the deconvolutional layer to obtain a preliminary acceleration effect. Secondly, the neural network is segmented by the optimizer to make full use of the computing resources on the edge and the cloud to further accelerate the inference of the neural network. In the first strategy, we achieve a 62.12% reduction in inference latency compared to the state-of-the-art object detection model Blazeface. Additionally, with a two-step speedup strategy, our method is only 26.5% of the baseline when the bandwidth is 500 kbps.

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confidence: 99%