计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 256-262.doi: 10.11896/jsjkx.190800159
王瑄, 毛莺池, 谢在鹏, 黄倩
WANG Xuan, MAO Ying-chi, XIE Zai-peng, HUANG Qian
摘要： 卷积神经网络(Convolutional Neural Network,CNN)作为深度学习的重要技术,已被广泛应用在移动智能应用中。针对CNN推断任务高内存、高计算量的需求,现有解决方案多将任务卸载到云上执行,难以适应时延敏感的移动应用程序。为解决上述问题,提出了一种基于改进差分进化算法的CNN推断任务卸载策略,它采用端云协作模式将计算任务部署在云和边缘设备之间。该策略研究了成本约束下最小化时延的任务卸载方案,将CNN推断过程转化为任务图并将其构建为0-1整数规划问题,利用改进二进制差分进化算法高效求解最佳卸载决策。实验结果表明,在给定费用约束下,与移动端推断和云推断方案相比,所提策略将任务响应时间平均缩短了33.60%和6.06%。
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