Designing convolutional neural networks (CNN) for mobile devices is
challenging because mobile models need to be small and fast, yet still
accurate. Although significant efforts have been dedicated to design and
improve mobile CNNs on all dimensions, it is very difficult to manually balance
these trade-offs when there are so many architectural possibilities to
consider. In this paper, we propose an automated mobile neural architecture
search (MNAS) approach, which explicitly incorporate model latency into the
main objective so that the search can identify a model that achieves a good
trade-off between accuracy and latency. Unlike previous work, where latency is
considered via another, often inaccurate proxy (e.g., FLOPS), our approach
directly measures real-world inference latency by executing the model on mobile
phones. To further strike the right balance between flexibility and search
space size, we propose a novel factorized hierarchical search space that
encourages layer diversity throughout the network. Experimental results show
that our approach consistently outperforms state-of-the-art mobile CNN models
across multiple vision tasks. On the ImageNet classification task, our MnasNet
achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8x
faster than MobileNetV2 [29] with 0.5% higher accuracy and 2.3x faster than
NASNet [36] with 1.2% higher accuracy. Our MnasNet also achieves better mAP
quality than MobileNets for COCO object detection. Code is at
this https URL

本文提出了一种自动化的移动神经结构搜索（MNAS）方法，该方法将模型延迟明确地纳入主要目标，以便搜索可以识别满足准确性和延迟之间良好平衡的模型， 实验结果表明，该方法在多个视觉任务中始终优于现有的移动卷积神经网络模型，并且实现了更好的 mAP 质量。