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Benefiting from the search efficiency, differentiable neural architecturesearch (NAS) has evolved as the most dominant alternative to automaticallydesign competitive deep neural networks (DNNs). We note that DNNs must beexecuted under strictly hard performance constraints in real-world scenarios,for example, the runtime latency on autonomous vehicles. However, to obtain thearchitecture that meets the given performance constraint, previoushardware-aware differentiable NAS methods have to repeat a plethora of searchruns to manually tune the hyper-parameters by trial and error, and thus thetotal design cost increases proportionally. To resolve this, we introduce alightweight hardware-aware differentiable NAS framework dubbed LightNAS,striving to find the required architecture that satisfies various performanceconstraints through a one-time search (i.e., \underline{\textit{you only searchonce}}). Extensive experiments are conducted to show the superiority ofLightNAS over previous state-of-the-art methods.

You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms