On Hardware-Aware Design and Optimization of Edge Intelligence

Abstract:Edge intelligence systems, the intersection of edge computing and artificial intelligence (AI), are pushing the frontier of AI applications. However, the complexity of deep learning models and heterogeneity of edge devices make the design of edge intelligence systems a challenging task. Hardware-agnostic methods face some limitations when implementing edge systems. Thus, hardware-aware methods are attracting more attention recently. In this paper, we present our recent endeavors in hardware-aware design and optimization for edge intelligence. We delve into techniques such as model compression and neural architecture search to achieve efficient and effective system designs. We also discuss some challenges in hardware-aware paradigm.

Author's accepted version. Published in IEEE Design & Test

Hardware Architecture (cs.AR); Machine Learning (cs.LG)

arXiv:2607.16297 [cs.AR]

(or arXiv:2607.16297v1 [cs.AR] for this version)

https://doi.org/10.48550/arXiv.2607.16297 Focus to learn more arXiv-issued DOI via DataCite

IEEE Design & Test, vol. 40, no. 6, pp. 149-162, December 2023

https://doi.org/10.1109/MDAT.2023.3307558 Focus to learn more DOI(s) linking to related resources

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