# Rapid and Accurate PPA Prediction for the Template-Based Processor Design Methods
## 4 Experimental Results
### PPA Prediction for the Parameter Set  
First, the samples are evenly sampled from the design space of the parameter set. Specifically, the number of parameters that are chosen to change ranges from 2 to 27, and for each case, there are 10 random samples. Therefore, there are \(26\times 10=260\) samples in total. Then, the ML-PM and MNL-PM are both used to estimate the PPA of these 260 samples. Moreover, the standard PPA value is also generated via synthesis and simulation flows, and Table 4 shows the PPA prediction performance for the parameter set of both the ML-PM and MNL-PM. Moreover, the accuracy is calculated via (17), in which the prediction is the PPA value obtained via the proposed NLR method andthe standard is the PPA value obtained via the EDA tools.  
\[Accuracy(\%)\ =(1-\frac{|prediction\ -standard|}{standard})\times 100\% \tag{17}\]  
The results show that MNL-PM performs better on the PPA estimation than ML-PM with higher accuracy and smaller standard deviation. Furthermore, the performance on the PPA prediction with the different number of changed parameters of ML-PM and MNL-PM is shown in Figure 3. The results shows that both proposed method works better when the number of changed parameter is less than 11, and the proposed method works mostly stable on the power prediction. As the results of Equations (1) and (11) are similar when \(\beta_{m}\) is small, the main reason for this fact is that the small value range of each parameter in the parameter set design space, which causes the influence of each parameter is little.  
In a real processor design process, the changes to an existing design in one time iteration usually appear in the same module. The samples of the parameter set design space in the experiment above cannot represent this behavior well. Therefore, the samples of the changed parameters in the same module are sampled in this section. In this experiment,there are 11, 5, 7, and 4 parameters in the Core, ICache, Dcache, and BTB modules, respectively. Taking the Core module as an example, the number of parameters that are chosen to change ranges from 2 to 11, and for each case, there are 4 random samples. In the same way, the samples for the other three modules are generated, and there are 92 samples. Moreover, the standard PPA value is also generated of these 92 samples in total. Then, the ML-PM and MNL-PM are both used to estimate the PPA via synthesis and simulation flows, and a scatter plot of the PPA prediction results for the parameter in the same module are shown in Figure 4.  
In Figure 5, MNL-PM performs better when the changed parameters are in the same module than randomly distributed.The results shows that there is irregular influence on the ML-PM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Model** & **Accuracy of Performance (\%)** & **Accuracy of Power (\%)** & **Accuracy of Area (\%)** \\ \hline ML-PM & \(98.42\pm 2.20\) & \(99.15\pm 0.97\) & \(98.53\pm 1.49\) \\ MNL-PM & \(98.60\pm 2.17\) & \(99.19\pm 0.83\) & \(98.68\pm 1.31\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The accuracy of the PPA prediction method.  
Figure 3: The performance on the PPA prediction with the different number of changed parameters. ((a) for performance, (b) for power, and, (c) for area).