量化是一種常見的模型壓縮技術，後訓練量化指的是在不進行進一步訓練的
情況下對預訓練模型進行量化。在本論文中，我們提出了兩種新穎的後訓練量
化方法。首先，我們利用比較激活值的相似性進行混合精度量化。其次，我們
引入了一種有效的零點調整方法，進一步提高了量化模型的準確性。實驗結果
顯示，與之前的方法相比，我們的方法表現更優。在將ResNet-18模型壓縮到相
同大小的情況下，我們的方法提高了1.7%的準確率。同樣地，在ResNet-50模型
上，我們的方法提高了3%的準確率。這些結果突出了我們的方法在提高量化模
型準確性方面的有效性。

Quantization is a common technique for model compression, where post-training quantization refers to quantizing a pre-trained model without further training. In this paper, we propose two novel methods for post-training quantization. Firstly, we leverage to compare the similarity of output feature map (OFM) to perform mixed-precision quantization on the model. Lastly, we introduce an effective zero-point adjustment method to enhance quantized models’ accuracy further. The
experimental results demonstrate the superiority of our approach compared to previous work. In the case of compressing the ResNet-18 model to the same size, our method achieves a 1.7% higher accuracy. Similarly, for the ResNet-50 model, our approach achieves a 3% accuracy improvement. These results highlight the effectiveness of our methods in improving the accuracy of quantized models.

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