ResMap: Exploiting Sparse Residual Feature Map for Accelerating Cross-Edge Video Analytics

Deploying deep convolutional neural network (CNN) to perform video analytics at edge poses a substantial system challenge, as running CNN inference incurs a prohibitive cost in computational resources. Model partitioning, as a promising approach, splits CNNs and distributes them to multiple edge devices in closer proximity to each other for serial inferences, however, it causes considerable cross-edge delay for transmitting intermediate feature maps. To overcome this challenge, we present ResMap, a new edge video analytics framework that significantly improves the cross-edge transmission and flexibly partitions the CNNs. Briefly, by exploiting the sparsity of the intermediate raw or residual feature map, ResMap effectively removes the redundant transmission, thereby decreasing the cross-edge transmission delay. In addition, ResMap incorporates an Online Data-Aware Scheduler to regularly update the CNN partitioning scheme so as to adapt to the time-varying edge runtime and video content. We have implemented ResMap fully based on COTS hardware, and the experimental results show that ResMap reduces the intermediate feature map volume by 14.93-46.12% and improves the average processing time by 17.43-30.6% compared to other alternative designs.