BLOX: Macro Neural Architecture Search Benchmark and Algorithms
Thomas Chun Pong Chau, Łukasz Dudziak, Hongkai Wen, Nicholas Donald Lane, Mohamed S Abdelfattah
Abstract
Neural architecture search (NAS) has been successfully used to design numerous high-performance neural networks. However, NAS is typically compute-intensive, so most existing approaches restrict the search to decide the operations and topological structure of a single block only, then the same block is stacked repeatedly to form an end-to-end model. Although such an approach reduces the size of search space, recent studies show that a macro search space, which allows blocks in a model to be different, can lead to better performance. To provide a systematic study of the performance of NAS algorithms on a macro search space, we release Blox – a benchmark that consists of 91k unique models trained on the CIFAR-100 dataset. The dataset also includes runtime measurements of all the models on a diverse set of hardware platforms. We perform extensive experiments to compare existing algorithms that are well studied on cell-based search spaces, with the emerging blockwise approaches that aim to make NAS scalable to much larger macro search spaces. The Blox benchmark and code are available at https://github.com/SamsungLabs/blox.
BibTeX
@inproceedings{
chau2022blox,
title={{BLOX}: Macro Neural Architecture Search Benchmark and Algorithms},
author={Thomas Chun Pong Chau and {\L}ukasz Dudziak and Hongkai Wen and Nicholas Donald Lane and Mohamed S Abdelfattah},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=IIbJ9m5G73t}
}