Edge-Cloud Cooperation for DNN Inference via Reinforcement Learning and Supervised Learning
This work addresses the problem of efficient DNN inference for IoT systems, offering a domain-specific solution that is incremental by combining existing RL and SL techniques.
The paper tackles the challenge of deploying heavyweight DNN models on resource-constrained edge devices by proposing an edge-cloud cooperation framework that uses RL-based compression and SL-based offloading, reducing inference latency by up to 78.8% and achieving higher accuracy compared to cloud-only strategies.
Deep Neural Networks (DNNs) have been widely applied in Internet of Things (IoT) systems for various tasks such as image classification and object detection. However, heavyweight DNN models can hardly be deployed on edge devices due to limited computational resources. In this paper, an edge-cloud cooperation framework is proposed to improve inference accuracy while maintaining low inference latency. To this end, we deploy a lightweight model on the edge and a heavyweight model on the cloud. A reinforcement learning (RL)-based DNN compression approach is used to generate the lightweight model suitable for the edge from the heavyweight model. Moreover, a supervised learning (SL)-based offloading strategy is applied to determine whether the sample should be processed on the edge or on the cloud. Our method is implemented on real hardware and tested on multiple datasets. The experimental results show that (1) The sizes of the lightweight models obtained by RL-based DNN compression are up to 87.6% smaller than those obtained by the baseline method; (2) SL-based offloading strategy makes correct offloading decisions in most cases; (3) Our method reduces up to 78.8% inference latency and achieves higher accuracy compared with the cloud-only strategy.