Current model quantization methods have shown their promising capability in
reducing storage space and computation complexity. However, due to the
diversity of quantization forms supported by different hardware, one limitation
of existing solutions is that usually require repeated optimization for
different scenarios. How to construct a model with flexible quantization forms
has been less studied. In this paper, we explore a one-shot network
quantization regime, named Elastic Quantization Neural Networks (EQ-Net), which
aims to train a robust weight-sharing quantization supernet. First of all, we
propose an elastic quantization space (including elastic bit-width,
granularity, and symmetry) to adapt to various mainstream quantitative forms.
Secondly, we propose the Weight Distribution Regularization Loss (WDR-Loss) and
Group Progressive Guidance Loss (GPG-Loss) to bridge the inconsistency of the
distribution for weights and output logits in the elastic quantization space
gap. Lastly, we incorporate genetic algorithms and the proposed Conditional
Quantization-Aware Accuracy Predictor (CQAP) as an estimator to quickly search
mixed-precision quantized neural networks in supernet. Extensive experiments
demonstrate that our EQ-Net is close to or even better than its static
counterparts as well as state-of-the-art robust bit-width methods. Code can be
available at
\href{https://github.com/xuke225/EQ-Net.git}{this https URL}.

本文提出一种名为 Elastic Quantization Neural Networks（EQ-Net）的一次性网络量化方案，旨在训练鲁棒的权重共享量化超网络。我们探索了一种弹性量化空间，并提出了权重分布正则化损失（WDR-Loss）和群体渐进引导损失（GPG-Loss）以解决弹性量化空间间隙中权重和输出逻辑的分布不一致问题。最后，我们结合了遗传算法和条件量化感知准确性预测器（CQAP），快速搜索超网络中混合精度量化的神经网络。大量实验证明，我们的 EQ-Net 几乎与或甚至更好地超过了静态对应物以及最先进的鲁棒位宽方法。