An Efficient Event-driven Inference Approach to Support AI Applications in IOWN Era

Abstract

Artificial intelligence (AI) in the Innovative Optical and Wireless Network (IOWN) era is expected to not only acquire capabilities beyond humans but also be energy-efficient, therefore contribute to the sustainability of future societies. This article describes an event-driven inference approach as a promising approach to balance AI capabilities and efficiency. This approach efficiently inspects continuous input stream data and generates events that trigger subsequent deeper inference tasks over geographically distributed computing resources only when they are truly necessary. This approach will significantly decrease energy consumption and computational and networking costs in AI inference.