Allowing Oscillation Quantization: Overcoming Solution Space Limitation in Low Bit-Width Quantization

Abstract

Quantization-aware Training (QAT) enables deep models to adapt to precision loss by simulating quantization. However, existing methods often converge to sub-optimal solutions due to inadequate exploration of quantization solution space. To address this, we propose a novel QAT method, Allowing Oscillation Quantization (AOQ), which expands the reachable solution space through weight oscillation. Notably, unlike previous methods that suppress oscillation throughout training, AOQ actively encourages it in the earlier stages to explore diverse quantization configurations, and suppresses it later to ensure convergence. In addition, by decoupling quantization thresholds and levels, AOQ promotes meaningful oscillation and improves the stability of learnable quantization parameters. Extensive experiments across various models, including ResNet, MobileNet, DeiT and Swin Transformer, demonstrate the effectiveness of our method. Specifically, with 2-bit quantization, AOQ achieves a 0.4% 2.2% accuracy improvement on ImageNet compared to state-of-the-art methods. Our implementation is available at https://github.com/muzenc/AOQ.