%0 Journal Article %A Qi FAN %A Xin CHEN %A Zhuo LI %T Inference delay optimization of branchy neural network model based on edge computing %D 2020 %R 10.11772/j.issn.1001-9081.2019081406 %J Journal of Computer Applications %P 342-346 %V 40 %N 2 %X

Aiming at the long delay of inference tasks in Deep Neural Network (DNN) on cloud servers, a branchy neural network deployment model based on edge computing was proposed. The distributed deployment problem of DNNs in edge computing scenarios was analyzed, and was proved to be NP-hard. A Deployment algorithm based on Branch and Bound (DBB) was designed to select appropriate edge computing nodes to reduce inference delay. And a Selection Node Exit (SNE) algorithm was designed and implemented to select the appropriate edge computing nodes for different tasks to exit the inference task. The simulation results show that, compared with the approach of deploying neural network model on the cloud, the branchy neural network model based on edge computing reduces the inference delay by 36% on average.

%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019081406