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