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The most significant barrier to the advancement of Neural Architecture Search(NAS) is its demand for large computational resources, which hindersscientifically sound empirical evaluations of NAS methods. Tabular NASbenchmarks have alleviated this problem substantially, making it possible toproperly evaluate NAS methods in seconds on commodity machines. However, anunintended consequence of tabular NAS benchmarks has been a focus on extremelysmall architectural search spaces since their construction relies on exhaustiveevaluations of the space. This leads to unrealistic results that do nottransfer to larger spaces. To overcome this fundamental limitation, we proposea methodology to create cheap NAS surrogate benchmarks for arbitrary searchspaces. We exemplify this approach by creating surrogate NAS benchmarks on theexisting tabular NAS-Bench-101 and on two widely used NAS search spaces with upto $10^{21}$ architectures ($10^{13}$ times larger than any previous tabularNAS benchmark). We show that surrogate NAS benchmarks can model the trueperformance of architectures better than tabular benchmarks (at a smallfraction of the cost), that they lead to faithful estimates of how welldifferent NAS methods work on the original non-surrogate benchmark, and thatthey can generate new scientific insight. We open-source all our code andbelieve that surrogate NAS benchmarks are an indispensable tool to extendscientifically sound work on NAS to large and exciting search spaces.

Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks