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2021

PTQ-SL: Exploring the Sub-layerwise Post-training Quantization

Yuan, Zhihang, Chen, Yiqi, Xue, Chenhao et al.

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Network quantization is a powerful technique to compress convolutional neural networks.

The quantization granularity determines how to share the scaling factors in weights, which affects the performance of network quantization.
Most existing approaches share the scaling factors layerwisely or channelwisely for quantization of convolutional layers.
Channelwise quantization and layerwise quantization have been widely used in various applications.

PTQ-SL: Exploring the Sub-layerwise Post-training Quantization · Around

Built on

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Earlier work this paper cites.
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Earlier work this paper cites.
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Wu, D.; Tang, Q.; Zhao, Y.; Zhang, M.; Fu, Y.; and Zhang, D. 2020 · 2006
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
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Choi, J.; Wang, Z.; Venkataramani, S.; Chuang, P. I.; Srinivasan, V.; and Gopalakrishnan, K. 2018 · 2018
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.

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