Batch Normalization in Quantized Networks

Eyyüb Sari; Vahid Partovi Nia

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arXiv · 2004.14214

Batch Normalization in Quantized Networks

Eyyüb Sari·Vahid Partovi Nia

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Abstract

Implementation of quantized neural networks on computing hardware leads to considerable speed up and memory saving. However, quantized deep networks are difficult to train and batch~normalization (BatchNorm) layer plays an important role in training full-precision and quantized networks. Most studies on BatchNorm are focused on full-precision networks, and there is little research in understanding BatchNorm affect in quantized training which we address here. We show BatchNorm avoids gradient explosion which is counter-intuitive and recently observed in numerical experiments by other researchers.

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Eyyüb Sari, Vahid Partovi Nia. 2020-04-29. Batch Normalization in Quantized Networks. https://arxiv.org/abs/2004.14214

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