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arXiv · 2201.06376

UWC: Unit-wise Calibration Towards Rapid Network Compression

Chen Lin·Zheyang Li·Bo Peng·Haoji Hu·Wenming Tan·Ye Ren·Shiliang Pu
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Abstract

This paper introduces a post-training quantization~(PTQ) method achieving highly efficient Convolutional Neural Network~ (CNN) quantization with high performance. Previous PTQ methods usually reduce compression error via performing layer-by-layer parameters calibration. However, with lower representational ability of extremely compressed parameters (e.g., the bit-width goes less than 4), it is hard to eliminate all the layer-wise errors. This work addresses this issue via proposing a unit-wise feature reconstruction algorithm based on an observation of second order Taylor series expansion of the unit-wise error. It indicates that leveraging the interaction between adjacent layers' parameters could compensate layer-wise errors better. In this paper, we define several adjacent layers as a Basic-Unit, and present a unit-wise post-training algorithm which can minimize quantization error. This method achieves near-original accuracy on ImageNet and COCO when quantizing FP32 models to INT4 and INT3.

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Chen Lin, Zheyang Li, Bo Peng, Haoji Hu, Wenming Tan, Ye Ren, Shiliang Pu. 2022-01-17. UWC: Unit-wise Calibration Towards Rapid Network Compression. https://arxiv.org/abs/2201.06376

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