  The expanding model size and computation of deep neural networks (DNNs) have
increased the demand for efficient model deployment methods. Quantization-aware
training (QAT) is a representative model compression method to leverage
redundancy in weights and activations. However, most existing QAT methods
require end-to-end training on the entire dataset, which suffers from long
training time and high energy costs. Coreset selection, aiming to improve data
efficiency utilizing the redundancy of training data, has also been widely used
for efficient training. In this work, we propose a new angle through the
coreset selection to improve the training efficiency of quantization-aware
training. Based on the characteristics of QAT, we propose two metrics: error
vector score and disagreement score, to quantify the importance of each sample
during training. Guided by these two metrics of importance, we proposed a
quantization-aware adaptive coreset selection (ACS) method to select the data
for the current training epoch. We evaluate our method on various networks
(ResNet-18, MobileNetV2), datasets(CIFAR-100, ImageNet-1K), and under different
quantization settings. Compared with previous coreset selection methods, our
method significantly improves QAT performance with different dataset fractions.
Our method can achieve an accuracy of 68.39% of 4-bit quantized ResNet-18 on
the ImageNet-1K dataset with only a 10% subset, which has an absolute gain of
4.24% compared to the baseline.
