Triple-partition Network: Collaborative Neural Network based on the ‘End Device-Edge-Cloud’

摘要

The traditional centralized data processing model represented by cloud computing cannot meet the data processing requirements that are gradually tending to the edge. Therefore, a new distributed computing model coordinated by the end devices, edges and cloud has become the main development direction. However, artificial intelligence algorithms that are widely used in cloud-only approach are difficult to embed in resource-constrained distributed frameworks. To address this issue, we propose Triple-partition Network, a neural network model augment with three exit points. The structure of three exit points allows to segment the traditional neural network and deploying them on the end devices, edges, and cloud. By setting up suitable exit points through the Entropy Topsis comprehensive evaluation model, part of the data can exit the network in advance to improve the efficiency of computing services. In this experiment, the classic neural networks (Alexnet, Resnet) are used to study the Triple-partition Network on a state-of-art platform and show that trained Triple-partition Network can greatly reduce the end-to-end latency by over 3x while achieving high accuracy.