For real time applications utilizing Deep Neural Networks (DNNs), it is
critical that the models achieve high-accuracy on the target task and
low-latency inference on the target computing platform. While Neural
Architecture Search (NAS) has been effectively used to develop low-latency
networks for image classification, there has been relatively little effort to
use NAS to optimize DNN architectures for other vision tasks. In this work, we
present what we believe to be the first proxyless hardware-aware search
targeted for dense semantic segmentation. With this approach, we advance the
state-of-the-art accuracy for latency-optimized networks on the Cityscapes
semantic segmentation dataset. Our latency-optimized small SqueezeNAS network
achieves 68.02% validation class mIOU with less than 35 ms inference times on
the NVIDIA AGX Xavier. Our latency-optimized large SqueezeNAS network achieves
73.62% class mIOU with less than 100 ms inference times. We demonstrate that
significant performance gains are possible by utilizing NAS to find networks
optimized for both the specific task and inference hardware. We also present
detailed analysis comparing our networks to recent state-of-the-art
architectures.

本研究采用无代理的硬件感知搜索方法，针对密集的语义分割任务，实现了特定任务和推理硬件上优化的网络，并在 Cityscapes 语义分割数据集上实现了最先进的网络准确度优化，在 NVIDIA AGX Xavier 上实现了低于 100ms 推理时间的 73.62％的类 mIOU，并与最新的最先进架构进行了详细分析。