Neural architecture search (NAS) enables researchers to automatically explore broad design spaces in order to improve efficiency of neural networks. This efficiency is especially important in the case of on-device deployment, where improvements in accuracy should be balanced out with computational demands of a model. In practice, performance metrics of model are computationally expensive to obtain. Previous work uses a proxy (e.g. number of operations) or a layer-wise measurement of neural network layers to estimate end-to-end hardware performance but the imprecise prediction diminishes the quality of NAS. To address this problem, we propose BRP-NAS, an efficient hardware-aware NAS enabled by an accurate performance predictor-based on graph convolutional network (GCN). What is more, we investigate prediction quality on different metrics and show that sample-efficiency of the predictor-based NAS can be improved by considering binary relations of models and an iterative data selection strategy. We show that our proposed method outperforms all prior methods on both NAS-Bench-101 and NAS-Bench-201. Finally, to raise awareness of the fact that accurate latency estimation is not a trivial task, we release LatBench - a latency dataset of NAS-Bench-201 models running on a broad range of devices.

提出了一种基于图卷积网络（GCN）的性能预测器，用于实现高效的硬件感知神经架构搜索（BRP-NAS）。与以往的方法不同，该方法重视模型之间的二元关系和迭代数据选择策略，实现了更高效的样本利用率。研究表明，该方法在多项飞行器搜索领域中的性能表现均优于现有方法，并呼吁人们重视准确的时延估计。

基于预测的图卷积神经网络NAS