Quantization-aware training (QAT) receives extensive popularity as it well retains the performance of quantized networks. In QAT, the contemporary experience is that all quantized weights are updated for an entire training process. In this paper, this experience is challenged based on an interesting phenomenon we observed. Specifically, a large portion of quantized weights reaches the optimal quantization level after a few training epochs, which we refer to as the partly scratch-off lottery ticket. This straightforward-yet-valuable observation naturally inspires us to zero out gradient calculations of these weights in the remaining training period to avoid meaningless updating. To effectively find the ticket, we develop a heuristic method, dubbed lottery ticket scratcher (LTS), which freezes a weight once the distance between the full-precision one and its quantization level is smaller than a controllable threshold. Surprisingly, the proposed LTS typically eliminates 50%-70% weight updating and 25%-35% FLOPs of the backward pass, while still resulting on par with or even better performance than the compared baseline. For example, compared with the baseline, LTS improves 2-bit MobileNetV2 by 5.05%, eliminating 46% weight updating and 23% FLOPs of the backward pass. Code is at url{https://github.com/zysxmu/LTS}.

翻译：量化感知训练因能良好保留量化网络性能而广受欢迎。在量化感知训练中，当前普遍经验是在整个训练过程中更新所有量化权重。本文基于观察到的有趣现象对这一经验提出质疑。具体而言，大量量化权重在少数训练周期后即达到最优量化水平，我们将其称为"部分划去式彩票票据"。这一简单而宝贵的观察自然启发我们在剩余训练阶段将这些权重的梯度计算归零，以避免无意义更新。为有效发现该票据，我们开发了一种启发式方法，称为"彩票票据刮除器"（LTS），当全精度权重与其量化水平之间的距离小于可控阈值时，即冻结该权重。令人惊讶的是，所提出的LTS通常能消除50%-70%的权重更新和25%-35%的反向传播FLOPs，同时保持与基线相当甚至更优的性能。例如，与基线相比，LTS在2比特MobileNetV2上提升了5.05%，消除了46%的权重更新和23%的反向传播FLOPs。代码见url{https://github.com/zysxmu/LTS}。