## 4 Experimental Results on DNNs
### Performance on Commonly Used Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dataset** & **\#Bit** & **Strategy** & **Acc-A** & **Acc-F** & **Acc-D** \\ \hline AlexNet & CIFAR-10 & 6 & U & 85.46 & 85.46 & 85.46 \\ AlexNet & CIFAR-10 & 4 to 8 & C+ & 85.51 & 85.88 & 85.27 \\ AlexNet & CIFAR-10 & 4 to 8 & C\(-\) & 86.00 & 85.27 & 85.88 \\ \hline ResNet-20 & CIFAR-10 & 6 & U & 91.51 & 91.51 & 91.51 \\ ResNet-20 & CIFAR-10 & 4 to 8 & C+ & 91.09 & 91.57 & 91.02 \\ ResNet-20 & CIFAR-10 & 4 to 8 & C\(-\) & 91.64 & 91.02 & 91.57 \\ \hline ResNet-20 & CIFAR-100 & 6 & U & 68.40 & 68.40 & 68.40 \\ ResNet-20 & CIFAR-100 & 4 to 8 & C+ & 68.37 & 68.60 & 68.33 \\ ResNet-20 & CIFAR-100 & 4 to 8 & C\(-\) & 68.71 & 68.33 & 68.60 \\ \hline ResNet-50 & ImageNet & 6 & U & 76.30 & 76.30 & 76.30 \\ ResNet-50 & ImageNet & 4 to 8 & C+ & 75.92 & 76.45 & 76.11 \\ ResNet-50 & ImageNet & 4 to 8 & C\(-\) & 76.56 & 76.11 & 76.45 \\ \hline MobileNet-V2 & ImageNet & 6 & U & 66.45 & 66.45 & 66.45 \\ MobileNet-V2 & ImageNet & 4 to 8 & C+ & 65.19 & 66.67 & 64.31 \\ MobileNet-V2 & ImageNet & 4 to 8 & C\(-\) & 66.79 & 64.31 & 66.67 \\ \hline MobileNet-V2 (\(\alpha=0.5\)) & ImageNet & 6 & U & 55.61 & 55.61 & 55.61 \\ MobileNet-V2 (\(\alpha=0.5\)) & ImageNet & 4 to 8 & C+ & 53.47 & 55.67 & 53.06 \\ MobileNet-V2 (\(\alpha=0.5\)) & ImageNet & 4 to 8 & C\(-\) & 55.85 & 52.13 & 55.67 \\ \hline Transformer & WikiText-103 & 7 & U & 32.30 & 32.30 & 33.09 \\ Transformer & WikiText-103 & 6 to 8 & C+ & 33.89 & 31.57 & 32.53 \\ Transformer & WikiText-103 & 6 to 8 & C\(-\) & 31.60 & 33.55 & 31.30 \\ \hline
2-LSTM & PTB & 7 & U & 97.98 & 97.98 & 97.98 \\
2-LSTM & PTB & 6 to 8 & C+ & 98.46 & 97.84 & 97.91 \\
2-LSTM & PTB & 6 to 8 & C\(-\) & 97.21 & 97.88 & 98.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Final training performance corresponding to bit-width strategies set according to the different factors. For instance, the value 85.51 in the second row and fifth column indicates that, with a positive correlation between the number of accumulations in matrix calculations and the computational bit-width (where higher accumulation counts in matrix operations correspond to a higher bit-width), the resulting test accuracy is 85.51 (‘Acc-A/‘Acc-F/‘Acc-D’ denotes the controlled factor is Accumulation in matrix calculations/‘Frequency of elements in matrix be used/Depth of Matrix in DNN). \(\alpha\) is the width multiplier defined in [65]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dataset** & **\#Bit** & **Strategy** & **Size-A** & **Size-F** & **Size-D** \\ \hline ResNet-20 & CIFAR-10/100 & 6 & U & 1.12 & 1.12 & 1.12 \\ ResNet-20 & CIFAR-10/100 & 4 to 8 & C+ & 1.08 & 1.05 & 1.20 \\ ResNet-20 & CIFAR-10/100 & 4 to 8 & C\(-\) & 1.01 & 1.20 & 1.05 \\ \hline ResNet-50 & ImageNet & 6 & U & 176 & 176 & 176 \\ ResNet-50 & ImageNet & 4 to 8 & C+ & 216 & 129 & 222 \\ ResNet-50 & ImageNet & 4 to 8 & C\(-\) & 140 & 222 & 129 \\ \hline Transformer & WikiText-103 & 7 & U & 991 & 991 & 991 \\ Transformer & WikiText-103 & 6 to 8 & C+ & 977 & 1076 & 963 \\ Transformer & WikiText-103 & 6 to 8 & C\(-\) & 1005 & 906 & 1019 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model size (MBit) corresponding to bit-width strategies set according to the different factors. For instance, the value 216 in the third row and fifth column indicates that, with a positive correlation between the number of accumulations in matrix calculations and the computational bit-width (where higher accumulation counts in matrix operations correspond to a higher bit-width), the size of the quantized model is 216 MBit (‘Size-A/‘Size-F/‘Size-D’ denotes the controlled factor is Accumulation in matrix calculations/‘Frequency of elements in matrix be used/Depth of Matrix in DNN). Figure 7 shows the test accuracy and variance of different strategies using ResNet-20 on CIFAR-10/100 and ResNet-50 on ImageNet from 10 replicates.