Abstract. Recently binary weight networks have attracted lots of attentions due to their high computational efficiency and small parameter size.
Yet they still suffer from large accuracy drops because of their limited
representation capacity. In this paper, we propose a novel semi-binary
decomposition method which decomposes a matrix into two binary matrices and a diagonal matrix. Since the matrix product of binary matrices
has more numerical values than binary matrix, the proposed semi-binary
decomposition has more representation capacity. Besides, we propose an
alternating optimization method to solve the semi-binary decomposition problem while keeping binary constraints. Extensive experiments
on AlexNet, ResNet-18, and ResNet-50 demonstrate that our method
outperforms state-of-the-art methods by a large margin (5 percentage
higher in top1 accuracy). We also implement binary weight AlexNet on
FPGA platform, which shows that our proposed method can achieve
? 9× speed-ups while reducing the consumption of on-chip memory and
dedicated multipliers significantly