论文标题
AOW:具有延迟约束的自适应和最佳网络宽度搜索
AOWS: Adaptive and optimal network width search with latency constraints
论文作者
论文摘要
神经体系结构搜索(NAS)方法旨在自动找到适合计算约束的新型CNN体系结构,同时在目标平台上保持良好的性能。我们介绍了一种新型的有效的单发NAS方法,以最佳地搜索频道编号,并在特定的硬件上给定延迟约束。我们首先表明我们可以使用黑框方法来估算特定推理平台的现实延迟模型,而无需低级访问推理计算。然后,我们设计一个成对的MRF来评分任何通道配置,并使用动态编程有效地解码最佳性能配置,从而为网络宽度搜索提供了最佳解决方案。最后,我们提出了一个自适应通道配置采样方案,以逐步将训练阶段逐渐专门针对目标计算约束。 ImageNet分类的实验表明,我们的方法可以找到适合不同目标平台上资源约束的网络,同时提高了对最先进的有效网络的准确性。
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.