目前市面上有越來越多的民生消費性產品，例如筆記型電腦、PDA、手機等皆採用電池來供電。消費者在購買這些產品時考量的因素除了產品的美觀、實用性之外，產品的待機時間也是一項重要的考量因素。因此隨身設備的省電設計是極重要的一環。
對這些隨身設備而言節省電力的方式主要分兩方面，一是硬體方面的改善，例如採用較省電的材質來減少電池的耗電或設計時簡化產品所需的硬體元件數目。另一種方法是從軟體方面設計，除了減少不必要的動作此方式之外，當硬體有支援多重電壓時，我們就可以採用降低使用的電壓來節省能源。決定哪個工作使用較低的電壓執行的排程演算法造成的scheduling overhead不應該被忽略，此scheduling overhead應該越短越好。
目前許多新式的microprocessor與flash memory chip都有支援多重電壓，而在讀寫memory時所消耗的能源占隨身設備的總消耗能源比例極大，加上日益增加的memory size，使得節省memory所消耗的能源變的越來越重要。
無線網路設備與嵌入式設備除了必須要節省設備本身的能源消耗之外，即時也是一項重要的考量因素。因此對這些設備而言設計者必須考量在滿足即時的條件下節省能源。
在此論文中我們從imprecise computation的觀點來探討節省能源的議題，並使用imprecise computation algorithm所提到的資料結構來加快排程所需的時間，進而提出兩種演算法，經過實驗結果得知，所提的演算法較前人所設計的演算法需要較少的排程時間與節省較多的能源。

There are more and more portable devices nowadays. Portable devices such as notebooks、PDAs and cell phones are powered by battery. Customers care not only the function and the appearance of products, but also the battery life. Therefore, designing power saving mechanism is an important issue.
The power saving mechanisms can be categorized in two dimensions. The first one is the hardware solution which making use of the material that consumes less energy or simplifying the number of components. The other one is the software approach. If the hardware supports multiple voltage levels, we can execute some operations with lower voltage to reduce energy consumption. Furthermore, the overhead produced by voltage switching should be as low as possible.
Advanced microprocessors and flash memory chips support multiple voltages now. The proportion of energy consumption for operations of read/write flash memory to total energy consumption is very high. As the increasing of the size of flash memory, it’s important to design a power saving mechanism for flash memory. For embedded systems, moreover, the real-time requirement is also a critical issue. Hence, a new approach should satisfy not only the power saving but also the real-time requirements.
In this thesis, we discuss the power saving problem from the viewpoint of imprecise computation. We adopt the data structure using in imprecise computation to speed up the scheduling, and proposed two algorithms. The simulation results show that our proposed scheduling algorithms perform better than Chang’s algorithm and consume less power.

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