Uniform-precision neural network quantization has gained popularity since it simplifies densely packed arithmetic unit for high computing capability. However, it ignores heterogeneous sensitivity to the impact of quantization errors across the layers, resulting in sub-optimal inference accuracy. This work proposes a novel neural architecture search called neural channel expansion that adjusts the network structure to alleviate accuracy degradation from ultra-low uniform-precision quantization. The proposed method selectively expands channels for the quantization sensitive layers while satisfying hardware constraints (e.g., FLOPs, PARAMs). Based on in-depth analysis and experiments, we demonstrate that the proposed method can adapt several popular networks channels to achieve superior 2-bit quantization accuracy on CIFAR10 and ImageNet. In particular, we achieve the best-to-date Top-1/Top-5 accuracy for 2-bit ResNet50 with smaller FLOPs and the parameter size.

该论文提出了一种神经网络架构搜索方法——神经通道扩展，该方法可以适应不同层对于量化误差的敏感度，并在满足硬件约束的情况下，有选择性地扩展量化敏感的层的通道数，从而实现在CIFAR10和ImageNet数据集上的2位量化最佳精度，并在较小的FLOPs和参数大小下取得了迄今为止最高的ResNet50 Top-1 / Top-5准确率。

超低均匀精度量化的自动网络适应