Pyramid: Enabling Hierarchical Neural Networks with Edge Computing

Machine learning (ML) is powering a rapidly-increasing number of web applications. As a crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model training and inference at the network edge on edge servers. Compared with centralized cloud AI, edge AI enables low-latency ML inference which is critical to many delay-sensitive web applications, e.g., web AR/VR, web gaming andWeb-of-Things applications. Existing studies of edge AI focused on resource and performance optimization in training and inference, leveraging edge computing merely as a tool to accelerate training and inference processes. However, the unique ability of edge computing to process data with context awareness, a powerful feature for building the web-of-things for smart cities, has not been properly explored. In this paper, we propose a novel framework named Pyramid that unleashes the potential of edge AI by facilitating homogeneous and heterogeneous hierarchical ML inferences. We motivate and present Pyramid with traffic prediction as an illustrative example, and evaluate it through extensive experiments conducted on two real-world datasets. The results demonstrate the superior performance of Pyramid neural networks in hierarchical traffic prediction and weather analysis.

Automated summary

Machine learning is widely used in web applications and edge computing plays a crucial role in enabling low-latency inference for delay-sensitive web applications. However, the potential of edge computing for building smart cities through context-aware data processing has not been fully explored. In this paper, a novel framework called Pyramid is proposed to unleash the potential of edge AI through hierarchical ML inference. The results demonstrate superior performance in traffic prediction and weather analysis.