Neural Architecture Search (NAS), together with model scaling, has shown
remarkable progress in designing high accuracy and fast convolutional
architecture families. However, as neither NAS nor model scaling considers
sufficient hardware architecture details, they do not take full advantage of
the emerging datacenter (DC) accelerators. In this paper, we search for fast
and accurate CNN model families for efficient inference on DC accelerators. We
first analyze DC accelerators and find that existing CNNs suffer from
insufficient operational intensity, parallelism, and execution efficiency.
These insights let us create a DC-accelerator-optimized search space, with
space-to-depth, space-to-batch, hybrid fused convolution structures with
vanilla and depthwise convolutions, and block-wise activation functions. On top
of our DC accelerator optimized neural architecture search space, we further
propose a latency-aware compound scaling (LACS), the first multi-objective
compound scaling method optimizing both accuracy and latency. Our LACS
discovers that network depth should grow much faster than image size and
network width, which is quite different from previous compound scaling results.
With the new search space and LACS, our search and scaling on datacenter
accelerators results in a new model series named EfficientNet-X. EfficientNet-X
is up to more than 2X faster than EfficientNet (a model series with
state-of-the-art trade-off on FLOPs and accuracy) on TPUv3 and GPUv100, with
comparable accuracy. EfficientNet-X is also up to 7X faster than recent RegNet
and ResNeSt on TPUv3 and GPUv100.

本文提出了一个基于数据中心加速器优化的神经网络架构搜索空间，并探讨了新型的复合比例尺度方法，提出了一种称为 EfficientNet-X 的新型模型系列，该模型在 TPUv3 和 GPUv100 上比现有的 RegNet 和 ResNeSt 速度均快至少 2 倍，同时具有可与其相媲美的准确性。