  Adder Neural Network (AdderNet) provides a new way for developing
energy-efficient neural networks by replacing the expensive multiplications in
convolution with cheaper additions (i.e.l1-norm). To achieve higher hardware
efficiency, it is necessary to further study the low-bit quantization of
AdderNet. Due to the limitation that the commutative law in multiplication does
not hold in l1-norm, the well-established quantization methods on convolutional
networks cannot be applied on AdderNets. Thus, the existing AdderNet
quantization techniques propose to use only one shared scale to quantize both
the weights and activations simultaneously. Admittedly, such an approach can
keep the commutative law in the l1-norm quantization process, while the
accuracy drop after low-bit quantization cannot be ignored. To this end, we
first thoroughly analyze the difference on distributions of weights and
activations in AdderNet and then propose a new quantization algorithm by
redistributing the weights and the activations. Specifically, the pre-trained
full-precision weights in different kernels are clustered into different
groups, then the intra-group sharing and inter-group independent scales can be
adopted. To further compensate the accuracy drop caused by the distribution
difference, we then develop a lossless range clamp scheme for weights and a
simple yet effective outliers clamp strategy for activations. Thus, the
functionality of full-precision weights and the representation ability of
full-precision activations can be fully preserved. The effectiveness of the
proposed quantization method for AdderNet is well verified on several
benchmarks, e.g., our 4-bit post-training quantized adder ResNet-18 achieves an
66.5% top-1 accuracy on the ImageNet with comparable energy efficiency, which
is about 8.5% higher than that of the previous AdderNet quantization methods.
