A Resource-Efficient Multiple Recognition Services Framework for IoT Devices

Abstract

Deploying the convolutional neural network (CNN) model on Internet of Things (IoT) devices to provide diverse recognition services has received increasing attention. Due to the limited storage, computing, and other resources of IoT devices, it has become mainstream to first train the CNN model on the edge/cloud server and then send the trained CNN to the IoT device. However, most existing related methods suffer from two limitations, (i) low performance due to service interference or insufficient mutual assistance, and (ii) large memory resources and switching resource overhead. To this end, this article proposes a resource-efficient multiple recognition services framework for IoT devices. The proposed framework is based on the edge server-assisted IoT device training of the CNN model, and the framework includes a deeper weight adaptation (DeepWAdapt) algorithm to mitigate service interference. The DeepWAdapt algorithm consists of a set of learnable masks, and by inserting these masks into the appropriate layers of the CNN model, it mitigates mutual interference between services caused by training a single CNN model for multiple services. Each service has a specific set of masks. These learnable masks work like keys for each service, selecting appropriate and specific features for each service from a shared feature set. Experimental results demonstrate that the DeepWAdapt outperforms other state-of-the-art methods on image-level classification services and pixel-level dense prediction services. Specifically, when executing 40 services based on ResNet18, the proposed DeepWAdapt achieves 66.82% F1-score on the CelebA dataset, which is +2.61% F1-score than the previous state-of-the-art result. In addition, compared with the routing method, our proposed DeepWAdapt also reduces network transmission traffic by approximately 35%.