提出新方法提升卷积网络低比特量化精度，8位以上几乎无损。
Quantization Range Estimation for Convolutional Neural Networks
- 将量化范围估计建模为分层局部极小的优化问题，保证局部凸性。
- 在ResNet系列和Inception-v3上实现8/6位量化几乎零精度损失，4位显著提升。
- 适用于对模型压缩有要求的部署场景，尤其适合移动端推理。
训练后量化可有效降低深度神经网络模型存储需求，但低比特量化保持精度仍是难题。本文提出一种范围估计方法，通过将范围估计转化为分层局部极小的量化误差最小化优化问题，证明该问题具有局部凸性，并设计高效搜索算法求解最优解。进一步将该算法应用于变换后的权重空间以提升实际性能。实验表明，该方法在ResNet系列模型和Inception-v3图像分类任务中普遍优于现有最先进方法。在8位和6位量化设置下，顶1准确率几乎无损失；4位量化精度也得到显著提升。代码已公开于https://github.com/codeiscommitting/REQuant。
原文摘要 · Abstract (English)
Post-training quantization for reducing the storage of deep neural network models has been demonstrated to be an effective way in various tasks. However, low-bit quantization while maintaining model accuracy is a challenging problem. In this paper, we present a range estimation method to improve the quantization performance for post-training quantization. We model the range estimation into an optimization problem of minimizing quantization errors by layer-wise local minima. We prove this problem is locally convex and present an efficient search algorithm to find the optimal solution. We propose the application of the above search algorithm to the transformed weights space to do further improvement in practice. Our experiments demonstrate that our method outperforms state-of-the-art performance generally on top-1 accuracy for image classification tasks on the ResNet series models and Inception-v3 model. The experimental results show that the proposed method has almost no loss of top-1 accuracy in 8-bit and 6-bit settings for image classifications, and the accuracy of 4-bit quantization is also significantly improved. The code is available at https://github.com/codeiscommitting/REQuant.
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