Binary Neural Networks (BNNs) use 1-bit weights and activations to
efficiently execute deep convolutional neural networks on edge devices.
Nevertheless, the binarization of the first layer is conventionally excluded,
as it leads to a large accuracy loss. The few works addressing the first layer
binarization, typically increase the number of input channels to enhance data
representation; such data expansion raises the amount of operations needed and
it is feasible only on systems with enough computational resources. In this
work, we present a new method to binarize the first layer using directly the
8-bit representation of input data; we exploit the standard bit-planes encoding
to extract features bit-wise (using depth-wise convolutions); after a
re-weighting stage, features are fused again. The resulting model is fully
binarized and our first layer binarization approach is model independent. The
concept is evaluated on three classification datasets (CIFAR10, SVHN and
CIFAR100) for different model architectures (VGG and ResNet) and, the proposed
technique outperforms state of the art methods both in accuracy and BMACs
reduction.

本文提出一种新方法，使用 8 位表示的输入数据直接对第一层进行二值化处理，并利用标准位平面编码按位提取特征，经过加权和再次融合后得到全二值化模型，该方法在不同神经网络体系结构下验证，比现有技术在准确性和 BMCA (二值乘法计算数) 减少方面更佳。