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Recently, artificial intelligence (AI) is undergoing a sustained success renaissance as it can substantially improve networks' cognitive performance and intelligence, thereby contributing to fully unleashing the potential of big data. Pushing the AI frontiers to the network edge in this context and trends has given rise to an emerging interdiscipline, namely, edge intelligence (EI). Indeed, EI can sink the cloud's processing capabilities to the edge side, and provide real-time response while enabling more intelligent services with high performance. However, the successful realization of EI is still in its infancy. Thus, this article aims to provide a comprehensive study of this young field from a broader perspective. We first discuss the prior knowledge based on which we take a holistic overview of EI, including its key concepts, advantages, and development trend. Then we highlight the collaboration modes in EI, and discuss two typical case categories. Subsequently, the entire processes of model training and inference in EI are elaborated. Finally, we discuss a typical application scenario and its specific embodiment of EI and strive to shed light on some potential challenges, which may facilitate the transformation of EI from theory to practice.
Jiang et al. (Wed,) studied this question.