Abstract: As an important technology of deep learning,convolutional Neural Network (CNN) has been widely used in intelligence applications.Due to the demand of CNN inference task for high computer memories and computation,most of the existing solutions are to offload tasks to the cloud for execution,which are hard to adapt to the time-delay sensitive mobile applications.To solve the above problem,this paper proposes a CNN inference task offloading strategy based on improved differential evolution algorithm,which can efficiently deploy computing tasks between cloud and edge devices using end-cloud collaboration mode.This strategy studies the task unloading scheme that minimizes the time delay under cost constraint.transforms the CNN inference process into a task graph and constructs it into a 0-1 integer programming problem,and finally uses the improved binary differential evolution algorithm to solve the problem so as to infer the optimal offloading policy.The experimental results show that,compared with mobile inference and cloud inference schemes,averagely,the proposed strategy can reduce the task response time by 33.60% and 6.06% respectively with cost constraints.

Key words: Collaborative inference, Computing offloading, Convolutional neural network, Differential evolution algorithm, Mobile cloud computing