非對稱性多處理器系統，通常是由一顆一般型處理器與一顆或多顆特殊運算能力的處理器所組成，隨著非對稱性多處理器系統的普及化，越來越多的嵌入式裝置，例如：手機、PDA等，都已經內建此非對稱性架構；另一方面，有許多的研究也在致力於整合此非對稱性架構，目的在藉由整合不同處理器的運算特性，不僅能夠達到相對於對稱性架構下更好的效能，也比一般使用單一高速處理器的方法來得省電與降低成本，尤其在嵌入式系統下，電池有限的蓄電量對於嵌入式攜帶式裝置來說，是非常大的限制；在非對稱性架構下為了達到省電與提高效能的目的，作業系統中的工作排程及顯得非常重要。
由於處理器硬體架構不同的特性，底層運算單元實做上的不同，相同的工作在異質性的處理器上面表現出的時間與耗能的表現上皆有所不同，當一件工作在面對不同的處理器執行時間與耗能，如何在兩者之間的平衡上做選擇，以及當面對系統中有m件無先後關係的工作以及n顆異質性處理器時，如何在一段特定的時間D內完成所有工作，以及使整體系統達到最低耗能，是本篇論文的目的。
同質性多處理器的排程演算法自古以來就是一個非常複雜的NP問題，而過去在同質性多處理器上的排程演算法，由於沒有考慮到不同的處理器執行時間的問題，當直接套用於異質性多處理器架構下時，自然在省電效能方面表現得非常的差；本篇論文分析各個處理器的執行速度與耗電功率的關係，配合同一件工作擁有所需不同的處理器執行週期，為每件工作找出能量與時間比例上最適合的處理器。
另外，因為工作與工作之間的互相影響，而使得排程方法變得複雜，本篇論文亦提出兩個不同時間複雜度的調整演算法，利用收斂的特性，目的在使得最後的結果能趨向於最佳解。

An asymmetric multi-processor system is usually composed of a general pur-
pose RISC processor and one or more con‾gurable computing unit such as DSP
cores. While asymmetric multi-processor system platform is getting much an much
more popular on most modern embedded devices, many researches are devoted to
constructing a °exible scheduling framework on such a platform. By parallel exe-
cuting tasks between di®erent computing units, we may be able to use two or more
lower speed processors than the traditional one high speed uni-processor system, if
and only if we schedule the tasks right on the suitable kind of processor. Every
kind of processors has its own power and speed relationship. Every task due to
di®erent implement algorithm and running on di®erent hardware implementation
also encounters di®erent execution clock cycle counts and spent di®erent quantity
of energy. The more clock cycle counts one processor should execute in one period,
the more speed and energy consumption a processor will have. How to equally and
e±ciently schedule tasks between heterogeneous processors to achieve a su±cient
performance requirement while making the energy consumption of the system min-
imum is our goal. This paper provide a task decision method that can evaluate the
energy performance index of one task on each processor under a frame based real-
time system. Besides we provide another load-balance algorithm which is needed
when the scheduling result is under an un-balanced load situation that might waste
the computing power of another processor.

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