There is a constant need for high-performing and computationally efficient neural network models for image super-resolution: computationally efficient models can be used via low-capacity devices and reduce carbon footprints. One way to obtain such models is to compress models, e.g. quantization. Another way is a neural architecture search that automatically discovers new, more efficient solutions. We propose a novel quantization-aware procedure, the QuantNAS that combines pros of these two approaches. To make QuantNAS work, the procedure looks for quantization-friendly super-resolution models. The approach utilizes entropy regularization, quantization noise, and Adaptive Deviation for Quantization (ADQ) module to enhance the search procedure. The entropy regularization technique prioritizes a single operation within each block of the search space. Adding quantization noise to parameters and activations approximates model degradation after quantization, resulting in a more quantization-friendly architectures. ADQ helps to alleviate problems caused by Batch Norm blocks in super-resolution models. Our experimental results show that the proposed approximations are better for search procedure than direct model quantization. QuantNAS discovers architectures with better PSNR/BitOps trade-off than uniform or mixed precision quantization of fixed architectures. We showcase the effectiveness of our method through its application to two search spaces inspired by the state-of-the-art SR models and RFDN. Thus, anyone can design a proper search space based on an existing architecture and apply our method to obtain better quality and efficiency. The proposed procedure is 30\% faster than direct weight quantization and is more stable.

翻译：图像超分辨率领域持续需要高性能且计算高效的神经网络模型：计算高效模型可通过低功耗设备部署，并减少碳足迹。实现此类模型的途径之一是模型压缩（如量化），另一种则是通过神经架构搜索自动发现更高效的新方案。我们提出一种新型量化感知流程QuantNAS，融合了上述两种方法的优势。为实现QuantNAS的有效运行，该流程专门搜索对量化友好的超分辨率模型。该方法通过熵正则化、量化噪声以及自适应量化偏差（ADQ）模块增强搜索过程。熵正则化技术优先选择搜索空间每个模块内的单一操作；对参数和激活值添加量化噪声可近似模拟量化后的模型退化，从而构建更适应量化的架构；ADQ则有助于缓解超分辨率模型中批归一化模块引发的问题。实验结果表明，相较于直接模型量化，本文提出的近似方法更适用于搜索过程。QuantNAS发现的架构在峰值信噪比（PSNR）与比特操作数（BitOps）的权衡上，优于固定架构的均匀量化或混合精度量化。我们通过将该方法应用于两个受最新超分辨率模型及RFDN启发的搜索空间，验证了其有效性。因此，研究者可基于现有架构设计合适的搜索空间，并应用本方法以获得更优的质量与效率。所提流程比直接权重量化快30%，且稳定性更高。