Context-aware adaptation of deep learning models for IoT devices

Abstract

The rapid development of both Artificial Intelligence (AI) and the Internet of Things (IoT), has cultivated the new research area: the Artificial Intelligence of Things (AIoT). AIoT is used to deploy many different deep learning models on a variety of local IoT terminals including smartphones, wearables, and other embedded devices. Adapting to these dynamic and varied AIoT application scenarios, and the IoT platform resources (e.g., computation and storage resources) available in each diverse, requires a novel scheme for improving on device environmental adaptability. Deep learning models aim to dynamically adjust either the model structure, the calculation scheme, or both, of them specifically to adapt to the environment context. They must reduce costs and improve computational efficiency while creating negligible performance degradation. Specifically, an environmental adaptation evolution framework must actively and continuously assess the constantly changing environmental context including factors, such as application data, knowledge base, task-related performance requirements, and platform-imposed resource constraints. Then it must adopt on-demand model compression, model segmentation, and domain adaptation techniques to achieve a appropriate balance between the models performance and the environments budget. This paper focuses on making deep learning models for context-aware adaptation. We discuss the system architecture and core technologies solving this problem requires. We address research challenges in this area, and introduce our pilot research practice in this field.