當在設計一個低耗能的即時系統時，我們需要考慮如何在有耗電量的限制下，選取能夠滿足使用者需求及耗電量限制的應用程式在這個即時系統上執行。其中，每個應用程式都是從一個相對應的應用程式族中選取出來的。所謂的應用程式族指的是一組應用程式的集合，這些在同一個應用程式族的軟體，他們有同樣的功能並且在相同的電腦硬體結構下執行，但是仍有些許的相異處。

為了設計一個低耗能的即時系統，我們可能在調校軟體耗電量的過程中，發展出一系列軟體，這些軟體會被歸類在同一個應用程式族中。在一個應用程式族裡的成員是被視為具有相同功能的，但是他們可能有高低品質的差異及不同的耗電量。舉例來說，一個程式設計者在編譯程式時，可以藉由下不同的參數來產生出不同版本的軟體.其中一個版本所編譯出的指令集可能執行的速度較快，但是耗電量大，而另一個版本雖然耗電量較小，但執行時間卻較長。

剛才已經提到過，為了要滿足在即時系統上耗電量及時間的限制，我們必須從每一個應用程式中選取一個適當的版本來達到這個目的。但是要從所有的應用程式族裡找到一組最佳組合是一個NP-hard的問題。因此，當整個在即時系統上的程式的週期都一樣時，我們提出一個虛擬多項式演算法(Pseudo-polynomial time algorithm)來解決這個問題。而當週期不完全相同時，我們則提出了一個誘導式演算法(Heuristic algorithm)。

更進一步，當我們考慮硬體在不同模式下的耗電量差異時，即當我們將即時系統的動態功率管理(RT-DPM)也用到系統中時，所要面對的情況又更複雜了。因此，當我們將RT-DPM用在即時系統上時,我們提供了一個估計應用程式耗電量的式子來達到耗電量的近似估計，再結合剛才的誘導式演算法，即可以有效縮短整個低耗能即時系統的設計時間。

To design an energy efficiency real-time system, it relates to chose appropriate applications running concurrently under the energy budget constraint from different application families. An application family is a set of software having common assets and sharing architectural properties.

For low power design, each member in the same family is designed to deliver different quality with different energy consumption. Many software implementation strategies are available to provide different quality level. For example,
programmers can configure the parameters of the compiler to
generate a set of codes for the same application. That is,
for the same application, we can generate a version of codes with high performance instructions, and another one with low performance instructions but consume less energy to meet specific energy budget. They are all regarded as members of the same application family.

To satisfy the energy and timing constraint of the real-time
system, we should chose a appropriate application from each application family. Since to choose the optimal combination of tasks from application families under timing and energy constraints is NP-hard, we propose a pseudo-polynomial time algorithm to find an optimal solution for the frame base task set and a heuristic algorithm for the general cases.

Furthermore, if we consider the energy consumption of devices when applying real-time Dynamic Power Management (RT-DPM) policy, it becomes a more difficult problem. So,
we propose another energy estimation equation to help the
designers to estimate the energy consumption of a task when
applying RT-DPM. After combing the heuristic algorithm and the energy estimation equation , the time complexity to find the solution of the problem when applying RT-DPM can be
reduced significantly.

Based on this algorithm, we developed a tool to provide constructive instructions in tuning system parameters for meeting a specific energy budget. We use an intelligent home case study to demonstrate its role in the low power
design cycle. The experiment results shows that the tool can significantly reduce the design cycle of a low-power real-time embedded system.

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