With the development of high-definition display devices, the practical
scenario of Super-Resolution (SR) usually needs to super-resolve large input
like 2K to higher resolution (4K/8K). To reduce the computational and memory
cost, current methods first split the large input into local patches and then
merge the SR patches into the output. These methods adaptively allocate a
subnet for each patch. Quantization is a very important technique for network
acceleration and has been used to design the subnets. Current methods train an
MLP bit selector to determine the propoer bit for each layer. However, they
uniformly sample subnets for training, making simple subnets overfitted and
complicated subnets underfitted. Therefore, the trained bit selector fails to
determine the optimal bit. Apart from this, the introduced bit selector brings
additional cost to each layer of the SR network. In this paper, we propose a
novel method named Content-Aware Bit Mapping (CABM), which can remove the bit
selector without any performance loss. CABM also learns a bit selector for each
layer during training. After training, we analyze the relation between the edge
information of an input patch and the bit of each layer. We observe that the
edge information can be an effective metric for the selected bit. Therefore, we
design a strategy to build an Edge-to-Bit lookup table that maps the edge score
of a patch to the bit of each layer during inference. The bit configuration of
SR network can be determined by the lookup tables of all layers. Our strategy
can find better bit configuration, resulting in more efficient mixed precision
networks. We conduct detailed experiments to demonstrate the generalization
ability of our method. The code will be released.

本文提出了一种名为内容感知比特映射的新方法，可以在训练期间学习每个层的比特选择器，构建适用于超分辨率网络的边缘到比特查找表，通过该策略可以找到更好的比特配置结果，使得混合精度网络更加高效。

针对大输入的单图像超分辨率网络的内容感知位映射技术

CABM: Content-Aware Bit Mapping for Single Image Super-Resolution  Network with Large Input

Efficient deep neural network (DNN) inference on mobile or embedded devices
typically involves quantization of the network parameters and activations. In
particular, mixed precision networks achieve better performance than networks
with homogeneous bitwidth for the same size constraint. Since choosing the
optimal bitwidths is not straight forward, training methods, which can learn
them, are desirable. Differentiable quantization with straight-through
gradients allows to learn the quantizer's parameters using gradient methods. We
show that a suited parametrization of the quantizer is the key to achieve a
stable training and a good final performance. Specifically, we propose to
parametrize the quantizer with the step size and dynamic range. The bitwidth
can then be inferred from them. Other parametrizations, which explicitly use
the bitwidth, consistently perform worse. We confirm our findings with
experiments on CIFAR-10 and ImageNet and we obtain mixed precision DNNs with
learned quantization parameters, achieving state-of-the-art performance.

本文提出一种不同的量化方法，使用不同的参数化方法来训练深度神经网络，从而达到更高效深度神经网络推理的效果。通过多组实验验证，使用该方法训练得到的量化参数可以达到最佳效果。