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arXiv · 1912.08883

Adaptive Loss-aware Quantization for Multi-bit Networks

Zhongnan Qu·Zimu Zhou·Yun Cheng·Lothar Thiele
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Abstract

We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms. We propose Adaptive Loss-aware Quantization (ALQ), a new MBN quantization pipeline that is able to achieve an average bitwidth below one-bit without notable loss in inference accuracy. Unlike previous MBN quantization solutions that train a quantizer by minimizing the error to reconstruct full precision weights, ALQ directly minimizes the quantization-induced error on the loss function involving neither gradient approximation nor full precision maintenance. ALQ also exploits strategies including adaptive bitwidth, smooth bitwidth reduction, and iterative trained quantization to allow a smaller network size without loss in accuracy. Experiment results on popular image datasets show that ALQ outperforms state-of-the-art compressed networks in terms of both storage and accuracy. Code is available at https://github.com/zqu1992/ALQ

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Zhongnan Qu, Zimu Zhou, Yun Cheng, Lothar Thiele. 2019-12-18. Adaptive Loss-aware Quantization for Multi-bit Networks. https://arxiv.org/abs/1912.08883

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