# CPU-GPU-Memory DVFS for Power-Efficient MPSoC in Mobile Cyber Physical Systems
## 6 Experimental Results
### Comparative Study between CGM-DVFS and Delayed-Reinforcement-Learning Approaches  
In this subsection, we provide a comparative study of our proposed method with the current state of the art.  
In Figure 4, Tables 3 and 4 it can be noted that CGM-DVFS outperforms the Q-Learning based reinforcement learning (RL) approach, Next, in which DVFS is only performed on the CPU and GPU. This was expected since CGM-DVFS performs DVFS on the CPU, GPU and RAM to reduce the power consumption even more. However, when compared to Next_Mod, in which DVFS is performed on the CPU, GPU & RAM using Q-Learning, CGM-DVFS outperformed this method for the yolo, render, stream, classify, blks., strm. and fft applications. Interestingly, Next_Mod seemed to produce sub-optimal (worse) results when compared to Next and CGM-DVFS, especially for the render and fft applications. This is due to the fact that for delayed RL approaches such as Q-Learning the agent must explore the dynamic system (dynamic environment) long enough to find the optimal outcome [16]. Although delayed RL approaches are beneficial to optimise the power consumption and temperature of the system in an application-agnostic manner, often, given the number of actions required (actions to perform DVFS on the CPU, GPU and RAM) if the agent is not allowed to explore the dynamic environment for long enough, then the method will result in sub-optimal power consumption. On the other hand, application-specific profiling approaches such as CGM-DVFS will result in close-to-optimal power consumption since these approaches are specific to certain applications.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**App** & **Perf.** & **MRPI** & **RProfiler.** & **Inter.** & **Next** & **N\_Mod.** \\ \hline face & 25.57 & 14.16 & 13.18 & 19.38 & 11.29 & 3.04 \\ yolo & 19.43 & 8.13 & 6.74 & 15.67 & 6.44 & 5.19 \\ render & 20.80 & 8.06 & 5.38 & 18.80 & 3.90 & 6.27 \\ stream & 13.93 & 6.428 & 3.83 & 12.16 & 3.99 & 1.82 \\ traffic & 23.25 & 8.71 & 3.96 & 16.58 & 2.63 & 0.38 \\ classify & **24.50** & **21.24** & 4.30 & 22.42 & 4.92 & 2.29 \\ blks. & 12.43 & 5.13 & 3.83 & 11.34 & 4.46 & 2.49 \\ strm. & 21.20 & 18.80 & 11.97 & 15.99 & 13.39 & 11.67 \\ fft & 12.21 & 9.04 & 9.42 & 11.40 & 10.95 & 12.08 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reduction in peak temperature of big CPUs (%) of CGM-DVFS compared to performance (perf.), MRPI, RewardProfiler (RProfiler), interactive (inter.), Next and Next_Mod (N_Mod.).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**App** & **Perf.** & **MRPI** & **RProfiler.** & **Inter.** & **Next** & **N\_Mod.** \\ \hline face & 21.08 & 14.59 & 9.28 & 17.81 & 8.61 & \(-\)3.17 \\ yolo & 24.46 & 8.79 & 4.049 & 19.82 & 2.16 & 1.89 \\ render & 18.00 & 9.40 & 8.39 & 15.34 & 8.12 & 8.89 \\ stream & 32.81 & 23.17 & 24.34 & 29.55 & 19.40 & 17.58 \\ traffic & 19.74 & 9.17 & 4.02 & 15.57 & 0.48 & \(-\)0.03 \\ classify & **33.48** & **26.80** & 15.36 & 30.2 & 11.42 & 6.93 \\ blks. & 12.43 & 5.13 & 3.83 & 11.34 & 6.10 & 5.10 \\ strm. & 21.20 & 18.80 & 11.97 & 15.99 & 2.62 & 0.60 \\ fft & 12.21 & 9.04 & 9.42 & 11.40 & 3.15 & 4.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Improvement in power-savings (%) of CGM-DVFS compared to performance (perf.), MRPI, RewardProfiler (RProfiler), interactive (inter.), Next and Next_Mod (N_Mod.).