Neural architecture search (NAS) approaches aim at automatically finding novel CNN architectures that fit computational constraints while maintaining a good performance on the target platform. We introduce a novel efficient one-shot NAS approach to optimally search for channel numbers, given latency constraints on a specific hardware. We first show that we can use a black-box approach to estimate a realistic latency model for a specific inference platform, without the need for low-level access to the inference computation. Then, we design a pairwise MRF to score any channel configuration and use dynamic programming to efficiently decode the best performing configuration, yielding an optimal solution for the network width search. Finally, we propose an adaptive channel configuration sampling scheme to gradually specialize the training phase to the target computational constraints. Experiments on ImageNet classification show that our approach can find networks fitting the resource constraints on different target platforms while improving accuracy over the state-of-the-art efficient networks.

本研究提出了一种高效的一次性神经结构搜索方法，通过优化搜索通道数以求在特定硬件上的延迟限制下，获得较好的神经结构性能，并通过适应性通道策略逐步满足目标计算约束。在ImageNet分类的实验中，该方法可以找到符合资源约束的网络解决方案，同时提高了有效网络的准确性，展示出新的颖展性。

AOWS: 自适应和优化的网络宽度搜索及延迟限制