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In the current AI era, mobile devices such as smartphones are tasked with executing a myriad of deep neural networks (DNNs) locally. It presents a complex landscape, as these models are highly fragmented in terms of architecture, operators, and implementations. Such fragmentation poses significant challenges to the co-optimization of hardware, systems, and algorithms for efficient and scalable mobile AI.
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Yuan et al. (Wed,) studied this question.
www.synapsesocial.com/papers/68e67cc0b6db643587606cf7 — DOI: https://doi.org/10.1145/3636534.3649361
Jinliang Yuan
Chen Yang
Dongqi Cai
Beijing University of Posts and Telecommunications
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