  Straight-through estimator (STE), which enables the gradient flow over the
non-differentiable function via approximation, has been favored in studies
related to quantization-aware training (QAT). However, STE incurs unstable
convergence during QAT, resulting in notable quality degradation in low
precision. Recently, pseudoquantization training has been proposed as an
alternative approach to updating the learnable parameters using the
pseudo-quantization noise instead of STE. In this study, we propose a novel
noise proxy-based integrated pseudoquantization (NIPQ) that enables unified
support of pseudoquantization for both activation and weight by integrating the
idea of truncation on the pseudo-quantization framework. NIPQ updates all of
the quantization parameters (e.g., bit-width and truncation boundary) as well
as the network parameters via gradient descent without STE instability.
According to our extensive experiments, NIPQ outperforms existing quantization
algorithms in various vision and language applications by a large margin.
