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In one-shot NAS, sub-networks need to be searched from the supernet to meetdifferent hardware constraints. However, the search cost is high and $N$ timesof searches are needed for $N$ different constraints. In this work, we proposea novel search strategy called architecture generator to search sub-networks bygenerating them, so that the search process can be much more efficient andflexible. With the trained architecture generator, given target hardwareconstraints as the input, $N$ good architectures can be generated for $N$constraints by just one forward pass without re-searching and supernetretraining. Moreover, we propose a novel single-path supernet, called unifiedsupernet, to further improve search efficiency and reduce GPU memoryconsumption of the architecture generator. With the architecture generator andthe unified supernet, we propose a flexible and efficient one-shot NASframework, called Searching by Generating NAS (SGNAS). With the pre-trainedsupernt, the search time of SGNAS for $N$ different hardware constraints isonly 5 GPU hours, which is $4N$ times faster than previous SOTA single-pathmethods. After training from scratch, the top1-accuracy of SGNAS on ImageNet is77.1%, which is comparable with the SOTAs. The code is available at:https://github.com/eric8607242/SGNAS.

Searching by Generating: Flexible and Efficient One-Shot NAS with Architecture Generator