论文标题
BCNN：二进制CNN，所有矩阵OPS都量化为1位精度
BCNN: A Binary CNN with All Matrix Ops Quantized to 1 Bit Precision
论文作者
论文摘要
本文描述了一个CNN，其中所有CNN样式的2D卷积操作较低至矩阵矩阵乘法是完全二进制的。该网络源自通用的构件结构，该结构与建设性的证明大纲一致，表明二进制神经网络是通用函数近似值。在2012年ImageNet验证集中的71.24％的前1位精度是通过2步训练程序实现的，并提供了针对二进制操作数优化的实施策略。
This paper describes a CNN where all CNN style 2D convolution operations that lower to matrix matrix multiplication are fully binary. The network is derived from a common building block structure that is consistent with a constructive proof outline showing that binary neural networks are universal function approximators. 71.24% top 1 accuracy on the 2012 ImageNet validation set was achieved with a 2 step training procedure and implementation strategies optimized for binary operands are provided.
