1.8k citations · 1.8k across the 3 of their papers we have counts for
3 papers
cs.CV2017★ 2 cited
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Shuchang Zhou, Yuzhi Wang, He Wen +2
Quantized Neural Networks (QNNs), which use low bitwidth numbers for representing parameters and performing computations, have been proposed to reduce the computation complexity, s…
cs.LG2016★ 64 cited
Effective Quantization Methods for Recurrent Neural Networks
Qinyao He, He Wen, Shuchang Zhou +4
Reducing bit-widths of weights, activations, and gradients of a Neural Network can shrink its storage size and memory usage, and also allow for faster training and inference by exp…
cs.NE2016★ 1.8k cited
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni +3
We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients. In particular, during…