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Multi-Objective Hardware Aware Neural Architecture Search using Hardware Cost Diversity
April 22, 2024, 4:41 a.m. | Nilotpal Sinha, Peyman Rostami, Abd El Rahman Shabayek, Anis Kacem, Djamila Aouada
cs.LG updates on arXiv.org arxiv.org
Abstract: Hardware-aware Neural Architecture Search approaches (HW-NAS) automate the design of deep learning architectures, tailored specifically to a given target hardware platform. Yet, these techniques demand substantial computational resources, primarily due to the expensive process of assessing the performance of identified architectures. To alleviate this problem, a recent direction in the literature has employed representation similarity metric for efficiently evaluating architecture performance. Nonetheless, since it is inherently a single objective method, it requires multiple runs to …
abstract architecture architectures arxiv automate computational cost cs.ai cs.lg deep learning demand design diversity hardware multi-objective nas neural architecture search performance platform process resources search type
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