Lightweight design of Convolutional Neural Networks (CNNs) requires co-design efforts in the model architectures and compression techniques. As a novel design paradigm that separates training and inference, a structural re-parameterized (SR) network such as the representative RepVGG revitalizes the simple VGG-like network with a high accuracy comparable to advanced and often more complicated networks. However, the merging process in SR networks introduces outliers into weights, making their distribution distinct from conventional networks and thus heightening difficulties in quantization. To address this, we propose an operator-level improvement for training called Outlier Aware Batch Normalization (OABN). Additionally, to meet the demands of limited bitwidths while upkeeping the inference accuracy, we develop a clustering-based non-uniform quantization framework for Quantization-Aware Training (QAT) named ClusterQAT. Integrating OABN with ClusterQAT, the quantized performance of RepVGG is largely enhanced, particularly when the bitwidth falls below 8.

翻译：卷积神经网络(CNNs)的轻量化设计需要在模型架构与压缩技术上进行协同优化。结构重参数化(SR)网络（以代表性网络RepVGG为例）作为一种分离训练与推理的新型设计范式，通过简单的VGG类网络实现了媲美先进且通常更复杂网络的高精度。然而，SR网络中的合并过程会在权重中引入离群值，使其分布异于传统网络，从而加剧量化难度。为此，本文提出一种面向训练的算子级改进——离群感知批归一化(OABN)。进一步地，为在保持推理精度的同时满足有限位宽需求，我们开发了一种基于聚类的非均匀量化感知训练(QAT)框架ClusterQAT。通过将OABN与ClusterQAT相结合，RepVGG在量化后的性能得到显著提升，尤其在位宽低于8比特时效果尤为突出。