Multiplication (e.g., convolution) is arguably a cornerstone of modern deep
neural networks (DNNs). However, intensive multiplications cause expensive
resource costs that challenge DNNs' deployment on resource-constrained edge
devices, driving several attempts for multiplication-less deep networks. This
paper presented ShiftAddNet, whose main inspiration is drawn from a common
practice in energy-efficient hardware implementation, that is, multiplication
can be instead performed with additions and logical bit-shifts. We leverage
this idea to explicitly parameterize deep networks in this way, yielding a new
type of deep network that involves only bit-shift and additive weight layers.
This hardware-inspired ShiftAddNet immediately leads to both energy-efficient
inference and training, without compromising the expressive capacity compared
to standard DNNs. The two complementary operation types (bit-shift and add)
additionally enable finer-grained control of the model's learning capacity,
leading to more flexible trade-off between accuracy and (training) efficiency,
as well as improved robustness to quantization and pruning. We conduct
extensive experiments and ablation studies, all backed up by our FPGA-based
ShiftAddNet implementation and energy measurements. Compared to existing DNNs
or other multiplication-less models, ShiftAddNet aggressively reduces over 80%
hardware-quantified energy cost of DNNs training and inference, while offering
comparable or better accuracies. Codes and pre-trained models are available at
this https URL

ShiftAddNet 是一种硬件启发式的深度神经网络，通过只使用位移和加权层替代传统的乘法运算来实现深度网络的显式参数化，从而获得与标准深度神经网络相当的表达能力和更加灵活的精度和效率之间的权衡，并且可以与量化和修剪等技术结合，实现在训练和推理中更加节能高效的性能。