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Shift neural networks reduce computation complexity by removing expensivemultiplication operations and quantizing continuous weights into low-bitdiscrete values, which are fast and energy efficient compared to conventionalneural networks. However, existing shift networks are sensitive to the weightinitialization, and also yield a degraded performance caused by vanishinggradient and weight sign freezing problem. To address these issues, we proposeS low-bit re-parameterization, a novel technique for training low-bit shiftnetworks. Our method decomposes a discrete parameter in a sign-sparse-shift3-fold manner. In this way, it efficiently learns a low-bit network with aweight dynamics similar to full-precision networks and insensitive to weightinitialization. Our proposed training method pushes the boundaries of shiftneural networks and shows 3-bit shift networks out-performs theirfull-precision counterparts in terms of top-1 accuracy on ImageNet.

$S^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks