Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation

TL;DR

This paper introduces N2UQ, a quantization method that combines the accuracy of nonuniform quantization with the efficiency of uniform quantization, using a novel G-STE for training and entropy regularization.

Contribution

N2UQ maintains nonuniform quantization accuracy while being hardware-friendly, utilizing learnable thresholds and a generalized straight-through estimator for effective training.

Findings

N2UQ outperforms state-of-the-art nonuniform methods by 0.5-1.7% on ImageNet.

The method achieves high accuracy with uniform weights and activations.

Efficient and hardware-friendly quantization approach demonstrated.

Abstract

The nonuniform quantization strategy for compressing neural networks usually achieves better performance than its counterpart, i.e., uniform strategy, due to its superior representational capacity. However, many nonuniform quantization methods overlook the complicated projection process in implementing the nonuniformly quantized weights/activations, which incurs non-negligible time and space overhead in hardware deployment. In this study, we propose Nonuniform-to-Uniform Quantization (N2UQ), a method that can maintain the strong representation ability of nonuniform methods while being hardware-friendly and efficient as the uniform quantization for model inference. We achieve this through learning the flexible in-equidistant input thresholds to better fit the underlying distribution while quantizing these real-valued inputs into equidistant output levels. To train the quantized network with learnable input thresholds, we introduce a generalized straight-through estimator (G-STE) for intractable backward derivative calculation w.r.t. threshold parameters. Additionally, we consider entropy preserving regularization to further reduce information loss in weight quantization. Even under this adverse constraint of imposing uniformly quantized weights and activations, our N2UQ outperforms state-of-the-art nonuniform quantization methods by 0.5~1.7 on ImageNet, demonstrating the contribution of N2UQ design. Code and models are available at: https://github.com/liuzechun/Nonuniform-to-Uniform-Quantization.