CiNet: Redesigning Deep Neural Networks for Efficient Mobile-Cloud Collaborative Inference
CiNet: Redesigning Deep Neural Networks for Efficient Mobile-Cloud Collaborative Inference
复制标题
DOI：
10.1137/1.9781611976700.52
复制
发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Xin Dai;Xiangnan Kong;Tian Guo;Yixian Huang
中科院分区：
文献类型：
--
作者：
Xin Dai;Xiangnan Kong;Tian Guo;Yixian Huang
深度神经网络越来越多地用于移动电话等终端设备中，以支持新的特征，例如图像分类fi阳离子。支持移动深度推理的传统模式分为基于云的模式或基于设备的模式-两者都需要访问整个预先培训的模式。因此，移动深度推理的ffi精度受到移动网络条件和计算能力的限制。协同推理是一种在移动设备和云服务器之间分割推理计算的方法，旨在通过图像压缩或模型分割等技术来解决传统推理的局限性。在本文中，我们从一个互补的方向来提高协作推理的性能，即通过重新设计深度神经网络来从一开始就满足协作需求。具体地，我们描述了一种用于图像分类的协作感知卷积神经网络的设计，称为CInet，fifi。CiNet由一个移动端提取器子模型和一个基于云的子模型组成，前者输出图像的一个小但相关的补丁，后者由Classifi在图像补丁上创建。我们从推理精度、计算代价和移动数据传输三个方面对CInet的ffi有效性进行了评估。结果表明，与传统推理方法和协作推理方法相比，CiNet的推理精度相当，而计算开销和传输数据量减少了数量级。
Deep neural networks are increasingly used in end devices such as mobile phones to support novel features, e.g., image classification. Traditional paradigms to support mobile deep inference fall into either cloud-based or on-device—both require access to an entire pre-trained model. As such, the efficacy of mobile deep inference is limited by mobile network conditions and computational capacity. Collaborative inference , a means to splitting inference computation between mobile devices and cloud servers, was proposed to address the limitations of traditional inference through techniques such as image compression or model partition. In this paper, we improve the performance of collaborative inference from a complementary direction, i.e., through redesigning deep neural networks to satisfy the collaboration requirement from the outset. Specifically, we describe the design of a collaboration-aware convolutional neural network, referred to as CiNet , for image classification. CiNet consists of a mobile-side extractor submodel that outputs a small yet relevant patch of the image and a cloud-based submodel that classifies on the image patch. Weevaluated the efficiency of CiNet in terms of inference accuracy, computational cost and mobile data transmission on three datasets. Our results demonstrate that CiNet achieved comparable inference accuracy while incurring orders of magnitude less computational cost and 99% less transmitted data, when comparing to both traditional and collaborative inference approaches.