## 4 Experimental Results and Analysis
### int16/int8/Mixed Quantization Experiment  
The total running time and recall rates of 8-bit, 16-bit, and mixed quantization are compared across the four validation datasets. The experimental results are shown in Table 8.  
The experimental results indicate that for Dataset1 and Dataset2, which have a high target average SNR, all three quantization methods achieve a 100% recall rate; for Dataset3, with a target average SNR of 7.74, the recall of all three quantization methods decreased to 94.12%; For Dataset4, with a target average SNR of 3.72, the recall of int16 and the mixed quantization methods decreased to 85.71%, and the recall of int8 quantization methods further decreased to 80.95%.  
The processing time experiment results are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Batch Size** & **Cos Sim.** & **Relative Dist.** \\ \hline
1 & 0.808 & 0.610 \\
2 & 0.836 & 0.580 \\
5 & 0.835 & 0.583 \\
10 & 0.835 & 0.583 \\
20 & 0.836 & 0.580 \\
100 & 0.839 & 0.579 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quantification effect of different batch sizes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Target Avg-SNR** & **Origin Recall Rate** & **Int8 Recall Rate** & **Int16 Recall Rate** & **Mixed Recall Rate** \\ \hline
1 & 53.86 & 100\% & 100\% & 100\% & 100\% \\
2 & 18.77 & 100\% & 100\% & 100\% & 100\% \\
3 & 7.74 & 100\% & 94.12\% & 94.12\% & 94.12\% \\
4 & 3.72 & 89.74\% & 80.95\% & 85.71\% & 85.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Recall rate results of the three quantization methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Quantization Bits** & **Dataset** & \begin{tabular}{c} **Memory Copy** \\ **Time/\(\mu\)s** \\ \end{tabular} & \begin{tabular}{c} **Hardware** \\ **Computation Time/\(\mu\)s** \\ \end{tabular} & \
\begin{tabular}{c} **Hardware** \\ **Inference Time/\(\mu\)s** \\ \end{tabular} &
\begin{tabular}{c} **Average** \\ **Reduction Time** \\ \end{tabular} \\ \hline \multirow{5}{*}{Int16} & 1 & 1587 & 1780 & 3358 & \multirow{5}{*}{-} \\  & 2 & 1623 & 1802 & 3425 & \\ \cline{1-1}  & 3 & 1590 & 1906 & 3496 & - \\ \cline{1-1}  & 4 & 1577 & 1846 & 3423 & \\ \cline{1-1}  & average & 1594 & 1833 & 3425 & \\ \hline \hline \multirow{5}{*}{Int8} & 1 & 998 & 1111 & 2109 & \multirow{5}{*}{37.99\%} \\  & 2 & 934 & 1212 & 2146 & \\ \cline{1-1}  & 3 & 969 & 1149 & 2118 & \\ \cline{1-1}  & 4 & 984 & 1139 & 2123 & \\ \cline{1-1}  & average & 971 & 1152 & 2124 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Inference time results of the three quantization methods.  
The experimental results demonstrate that the average inference time for int8 quantization is reduced by 37.99% compared to int16 quantization, while the average inference time for mixed quantization is 30.65% less than that of int16 quantization. The mixed quantization method not only decreases the hardware inference time but also maintains the same recall rate as int16 quantization. After deploying the mixed quantization, the comparison of the total running time is presented in Table 10, where FPS increased to 54.10.  
After network deployment, the FPGA resource usage of the hardware platform is shown in Table 11. It can be seen that the entire system has less hardware resource utilization and a lower resource consumption ratio.