Computer Science > Machine Learning
arXiv:2208.14446 (cs)
[Submitted on 30 Aug 2022]
Title:You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms
View a PDF of the paper titled You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms, by Xiangzhong Luo and 5 other authors
View PDFAbstract:Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs). We note that DNNs must be executed under strictly hard performance constraints in real-world scenarios, for example, the runtime latency on autonomous vehicles. However, to obtain the architecture that meets the given performance constraint, previous hardware-aware differentiable NAS methods have to repeat a plethora of search runs to manually tune the hyper-parameters by trial and error, and thus the total design cost increases proportionally. To resolve this, we introduce a lightweight hardware-aware differentiable NAS framework dubbed LightNAS, striving to find the required architecture that satisfies various performance constraints through a one-time search (i.e., \underline{\textit{you only search once}}). Extensive experiments are conducted to show the superiority of LightNAS over previous state-of-the-art methods.
| Comments: | Accepted by ACM/IEEE Design Automation Conference (DAC) 2022 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2208.14446 [cs.LG] |
| (orarXiv:2208.14446v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2208.14446 arXiv-issued DOI via DataCite |
Submission history
From: Xiangzhong Luo Mr. [view email][v1] Tue, 30 Aug 2022 02:23:23 UTC (2,777 KB)
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View a PDF of the paper titled You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms, by Xiangzhong Luo and 5 other authors
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