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。

SqueezeNAS：用于更快语义分割的快速神经架构搜索

SqueezeNAS: Fast neural architecture search for faster semantic  segmentation

The application of deep learning techniques resulted in remarkable
improvement of machine learning models. In this paper provides detailed
characterizations of deep learning models used in many Facebook social network
services. We present computational characteristics of our models, describe high
performance optimizations targeting existing systems, point out their
limitations and make suggestions for the future general-purpose/accelerated
inference hardware. Also, we highlight the need for better co-design of
algorithms, numerics and computing platforms to address the challenges of
workloads often run in data centers.

本文详细介绍了 Facebook 社交网络服务中使用的深度学习模型的计算特征，并描述了针对现有系统的高性能优化，同时指出了其局限性，并为未来的通用 / 加速推理硬件提出建议。此外，我们强调了算法、数字和计算平台更好的协同设计来解决数据中心中经常运行的工作负载的挑战。