ESAI: Efficient Split Artificial Intelligence via Early Exiting Using Neural Architecture Search

• URL: http://arxiv.org/abs/2106.12549v1
• Date: Mon, 21 Jun 2021 04:47:53 GMT
• Title: ESAI: Efficient Split Artificial Intelligence via Early Exiting Using Neural Architecture Search
• Authors: Behnam Zeinali, Di Zhuang, J. Morris Chang
• Abstract summary: Deep neural networks have been outperforming conventional machine learning algorithms in many computer vision-related tasks. The majority of devices are harnessing the cloud computing methodology in which outstanding deep learning models are responsible for analyzing the data on the server. In this paper, a new framework for deploying on IoT devices has been proposed which can take advantage of both the cloud and the on-device models.
• Score: 6.316693022958222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep neural networks have been outperforming conventional machine learning algorithms in many computer vision-related tasks. However, it is not computationally acceptable to implement these models on mobile and IoT devices and the majority of devices are harnessing the cloud computing methodology in which outstanding deep learning models are responsible for analyzing the data on the server. This can bring the communication cost for the devices and make the whole system useless in those times where the communication is not available. In this paper, a new framework for deploying on IoT devices has been proposed which can take advantage of both the cloud and the on-device models by extracting the meta-information from each sample's classification result and evaluating the classification's performance for the necessity of sending the sample to the server. Experimental results show that only 40 percent of the test data should be sent to the server using this technique and the overall accuracy of the framework is 92 percent which improves the accuracy of both client and server models.

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