PROFIT: A Novel Training Method for sub-4-bit MobileNet Models

• URL: http://arxiv.org/abs/2008.04693v1
• Date: Tue, 11 Aug 2020 13:29:50 GMT
• Title: PROFIT: A Novel Training Method for sub-4-bit MobileNet Models
• Authors: Eunhyeok Park and Sungjoo Yoo
• Abstract summary: 4-bit and lower precision mobile models are required due to the ever-increasing demand for better energy efficiency in mobile devices. We report that the activation instability induced by weight quantization (AIWQ) is the key obstacle to sub-4-bit quantization of mobile networks. To alleviate the AIWQ problem, we propose a novel training method called PROgressive-Freezing Iterative Training (PROFIT)
• Score: 14.328192808415853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 4-bit and lower precision mobile models are required due to the ever-increasing demand for better energy efficiency in mobile devices. In this work, we report that the activation instability induced by weight quantization (AIWQ) is the key obstacle to sub-4-bit quantization of mobile networks. To alleviate the AIWQ problem, we propose a novel training method called PROgressive-Freezing Iterative Training (PROFIT), which attempts to freeze layers whose weights are affected by the instability problem stronger than the other layers. We also propose a differentiable and unified quantization method (DuQ) and a negative padding idea to support asymmetric activation functions such as h-swish. We evaluate the proposed methods by quantizing MobileNet-v1, v2, and v3 on ImageNet and report that 4-bit quantization offers comparable (within 1.48 % top-1 accuracy) accuracy to full precision baseline. In the ablation study of the 3-bit quantization of MobileNet-v3, our proposed method outperforms the state-of-the-art method by a large margin, 12.86 % of top-1 accuracy.

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