Revisiting EdgeAI through the lens of communication, storage and computing optimisations
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Abstract
Implementing artificial intelligence models on edge devices (EdgeAI) has gained significant popularity due to its potential to enable real-time applications, achieve low latency, and conserve bandwidth. Additionally, reducing dependence on internet connections or cloud infrastructure provides a more secure and reliable execution environment. However, the resource-limited nature of edge devices poses challenges in communication, storage, and computing. Addressing these challenges requires a comprehensive understanding of existing domain optimisation strategies. This survey reviews the current state of the art in EdgeAI optimisation, focusing on communication protocols, storage solutions, and computing architectures that enhance performance and energy efficiency. The contributions of this review are twofold: (i) We highlight key trends, identify gaps in existing research, and propose promising directions for future research to improve the deployment and performance of EdgeAI systems further. (ii) We develop a structured taxonomy that categorises optimisation strategies into computing, storage, communication, and cross-cutting optimisations, offering a clear framework to understand their interrelated approaches and serving as a comparative framework to identify gaps that single-domain surveys often overlook. This survey is a valuable resource for researchers and practitioners seeking to navigate the complex landscape of EdgeAI optimisation and understand the impact of various optimisation pillars and their interactions.
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Accepted 2026-02-13
Published 2026-05-21
References
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