The results ofAddNN are basically consistent with the results in [1]. The accuracy drops after post-training quantization are reported in Table4.

A.3 Analysis on the Ratio of Discarded Outliers As we discussed in the subsection of outliers clamp for activations, the value Table 3: Analysis on the ratio of discarded outliers in activations. Besides, the comparisons with more CNN quantization methods are also supplemented. In Figure 1, we visualize the histogram of the weights and activations in AdderNet. Our AdderNet quantization method has one major limitation: as the number of bits decreases, the accuracy loss of the quantization model will increase. As for the societal impacts, the proposed quantization method can further reduce the energy consumption of AdderNet with a lower quantized accuracy loss.