Overcoming Oscillations in Quantization-Aware Training

• URL: http://arxiv.org/abs/2203.11086v1
• Date: Mon, 21 Mar 2022 16:07:42 GMT
• Title: Overcoming Oscillations in Quantization-Aware Training
• Authors: Markus Nagel, Marios Fournarakis, Yelysei Bondarenko, Tijmen Blankevoort
• Abstract summary: When training neural networks with simulated quantization, quantized weights can, rather unexpectedly, oscillate between two grid-points. We show that it can lead to a significant accuracy degradation due to wrongly estimated batch-normalization statistics. We propose two novel QAT algorithms to overcome oscillations during training: oscillation dampening and iterative weight freezing.
• Score: 18.28657022169428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When training neural networks with simulated quantization, we observe that quantized weights can, rather unexpectedly, oscillate between two grid-points. The importance of this effect and its impact on quantization-aware training are not well-understood or investigated in literature. In this paper, we delve deeper into the phenomenon of weight oscillations and show that it can lead to a significant accuracy degradation due to wrongly estimated batch-normalization statistics during inference and increased noise during training. These effects are particularly pronounced in low-bit ($\leq$ 4-bits) quantization of efficient networks with depth-wise separable layers, such as MobileNets and EfficientNets. In our analysis we investigate several previously proposed quantization-aware training (QAT) algorithms and show that most of these are unable to overcome oscillations. Finally, we propose two novel QAT algorithms to overcome oscillations during training: oscillation dampening and iterative weight freezing. We demonstrate that our algorithms achieve state-of-the-art accuracy for low-bit (3 & 4 bits) weight and activation quantization of efficient architectures, such as MobileNetV2, MobileNetV3, and EfficentNet-lite on ImageNet.

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