  We formalize and analyze a fundamental component of differentiable neural
architecture search (NAS): local "operation scoring" at each operation choice.
We view existing operation scoring functions as inexact proxies for accuracy,
and we find that they perform poorly when analyzed empirically on NAS
benchmarks. From this perspective, we introduce a novel
\textit{perturbation-based zero-cost operation scoring} (Zero-Cost-PT)
approach, which utilizes zero-cost proxies that were recently studied in
multi-trial NAS but degrade significantly on larger search spaces, typical for
differentiable NAS. We conduct a thorough empirical evaluation on a number of
NAS benchmarks and large search spaces, from NAS-Bench-201, NAS-Bench-1Shot1,
NAS-Bench-Macro, to DARTS-like and MobileNet-like spaces, showing significant
improvements in both search time and accuracy. On the ImageNet classification
task on the DARTS search space, our approach improved accuracy compared to the
best current training-free methods (TE-NAS) while being over 10$\times$ faster
(total searching time 25 minutes on a single GPU), and observed significantly
better transferability on architectures searched on the CIFAR-10 dataset with
an accuracy increase of 1.8 pp. Our code is available at:
https://github.com/zerocostptnas/zerocost_operation_score.
