  Neural architecture search (NAS), an important branch of automatic machine
learning, has become an effective approach to automate the design of deep
learning models. However, the major issue in NAS is how to reduce the large
search time imposed by the heavy computational burden. While most recent
approaches focus on pruning redundant sets or developing new search
methodologies, this paper attempts to formulate the problem based on the data
curation manner. Our key strategy is to search the architecture using
summarized data distribution, i.e., core-set. Typically, many NAS algorithms
separate searching and training stages, and the proposed core-set methodology
is only used in search stage, thus their performance degradation can be
minimized. In our experiments, we were able to save overall computational time
from 30.8 hours to 3.5 hours, 8.8x reduction, on a single RTX 3090 GPU without
sacrificing accuracy.
