Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However, advanced binary architectures still incorporate millions of inefficient and nonhardware-friendly full-precision multiplication operations. A&B BNN is proposed to directly remove part of the multiplication operations in a traditional BNN and replace the rest with an equal number of bit operations, introducing the mask layer and the quantized RPReLU structure based on the normalizer-free network architecture. The mask layer can be removed during inference by leveraging the intrinsic characteristics of BNN with straightforward mathematical transformations to avoid the associated multiplication operations. The quantized RPReLU structure enables more efficient bit operations by constraining its slope to be integer powers of 2. Experimental results achieved 92.30%, 69.35%, and 66.89% on the CIFAR-10, CIFAR-100, and ImageNet datasets, respectively, which are competitive with the state-of-the-art. Ablation studies have verified the efficacy of the quantized RPReLU structure, leading to a 1.14% enhancement on the ImageNet compared to using a fixed slope RLeakyReLU. The proposed add&bit-operation-only BNN offers an innovative approach for hardware-friendly network architecture.

二进制神经网络使用1位量化的权重和激活来减少模型的存储需求和计算负担。本文提出了A&B BNN，在传统的BNN中直接移除了一部分乘法运算并用相等数量的位运算代替其余部分，引入了掩码层和基于无正则化网络架构的量化RPReLU结构。掩码层可以通过数学变换避免相关的乘法运算。量化RPReLU结构通过限制其斜率为2的整数幂，实现更高效的位运算。实验结果在CIFAR-10、CIFAR-100和ImageNet数据集上分别达到92.30%、69.35%和66.89%，与最先进方法相当。消融实验验证了量化RPReLU结构的有效性，与使用固定斜率RLeakyReLU相比，在ImageNet上提升了1.14%。提出的仅添加位操作的BNN为硬件友好型网络架构提供了创新的方法。

A&B BNN: 仅支持加和位运算的硬件友好型二进制神经网络