Operators devoid of multiplication, such as Shift and Add, have gained prominence for their compatibility with hardware. However, neural networks (NNs) employing these operators typically exhibit lower accuracy compared to conventional NNs with identical structures. ShiftAddAug uses costly multiplication to augment efficient but less powerful multiplication-free operators, improving performance without any inference overhead. It puts a ShiftAdd tiny NN into a large multiplicative model and encourages it to be trained as a sub-model to obtain additional supervision. In order to solve the weight discrepancy problem between hybrid operators, a new weight sharing method is proposed. Additionally, a novel two stage neural architecture search is used to obtain better augmentation effects for smaller but stronger multiplication-free tiny neural networks. The superiority of ShiftAddAug is validated through experiments in image classification and semantic segmentation, consistently delivering noteworthy enhancements. Remarkably, it secures up to a 4.95% increase in accuracy on the CIFAR100 compared to its directly trained counterparts, even surpassing the performance of multiplicative NNs.

ShiftAddAug使用昂贵的乘法增强了高效但较弱的无乘法操作符，通过将ShiftAdd小型神经网络用作大型乘法模型的子模型进行训练，并鼓励它获得额外的监督，以解决混合操作符之间的权重差异问题。此外，采用新的权重共享方法和新的两阶段神经架构搜索方法，获得更好的增强效果以用于更小但更强大的无乘法小型神经网络。在图像分类和语义分割实验中，验证了ShiftAddAug的优越性，持续提供显著的改进。特别是在CIFAR100上，相较于直接训练的对照组，其精度提高了4.95%，甚至超过了乘法神经网络的性能。

ShiftAddAug: 用混合计算对无乘法小型神经网络进行增强