We propose a Quantization Guided Training (QGT) method to guide DNN training
towards optimized low-bit-precision targets and reach extreme compression
levels below 8-bit precision. Unlike standard quantization-aware training (QAT)
approaches, QGT uses customized regularization to encourage weight values
towards a distribution that maximizes accuracy while reducing quantization
errors. One of the main benefits of this approach is the ability to identify
compression bottlenecks. We validate QGT using state-of-the-art model
architectures on vision datasets. We also demonstrate the effectiveness of QGT
with an 81KB tiny model for person detection down to 2-bit precision
(representing 17.7x size reduction), while maintaining an accuracy drop of only
3% compared to a floating-point baseline.

本文提出了一种 Quantization Guided Training（QGT）方法，采用定制的正则化策略来鼓励权重值分布最大化精确度，减少量化误差，进而达到高效的低位精度压缩，并可用于检测压缩瓶颈，通过基于视觉数据集的现代模型和一个 81KB 的精度为 2 比特的小型模型进行有效性验证。