  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.
