Quantizing deep neural networks is an effective method for reducing memory
consumption and improving inference speed, and is thus useful for
implementation in resource-constrained devices. However, it is still hard for
extremely low-bit models to achieve accuracy comparable with that of
full-precision models. To address this issue, we propose learnable companding
quantization (LCQ) as a novel non-uniform quantization method for 2-, 3-, and
4-bit models. LCQ jointly optimizes model weights and learnable companding
functions that can flexibly and non-uniformly control the quantization levels
of weights and activations. We also present a new weight normalization
technique that allows more stable training for quantization. Experimental
results show that LCQ outperforms conventional state-of-the-art methods and
narrows the gap between quantized and full-precision models for image
classification and object detection tasks. Notably, the 2-bit ResNet-50 model
on ImageNet achieves top-1 accuracy of 75.1% and reduces the gap to 1.7%,
allowing LCQ to further exploit the potential of non-uniform quantization.

本文提出了一种可学习的压缩量化方法 (LCQ), 该方法能够灵活地通过优化模型权重和可学习的压缩函数来控制权重和激活的压缩级别，从而优于传统的最先进方法，并缩小量化模型与全精度模型之间的差距。