本論文之研究主題為複雜架構處理器之編譯技術。此類複雜處理器，為達到現代嵌入式系統之高效能、低耗電的艱鉅需求，在架構設計上產生許多奇特之處：不規則之資料運算路徑設計、分散式暫存器、簡化之內部資料線路、等。這些設計反應在其指令集架構，對於編譯器之設計造成相當的挑戰。此論文中實作之部分是以Open Research Compiler (ORC) 開放原始碼編譯器為基礎。我們也將詳細介紹此論文中主題之複雜處理器，叫做Parallel Architecture Core (PAC)。
我們將檢視複雜架構之處理器的機器模型，發現在此類處理器架構之中，內部的硬體資源有呈現結構性特質，使得除了傳統的指令排程之外，運算資料的傳輸亦成為新的課題。我們將以PAC為主例，探討複雜處理器架構之程式編譯技術。我們將ORC編譯器，移植至PAC處理器架構，為其產生可執行程式碼。為了產生PAC之程式，我們將提出以機器學習做為程式最佳化之方法的演算法，並且在ORC中實作。我們將數個數位訊號處理的測試程式加以編譯，並執行於PAC架構之模擬器上，以觀察處理器架構及編譯器之效能。測試結果顯示我們的機器學習演算法，相較於早先實作之簡易演算法，有效的增加了所產生程式之處理效率，減少執行時間，幅度約35%至40%。

In this thesis, we describe a method of instruction scheduling and operand placement for complex processor architectures. Such processors possess irregular datapaths, multiple register banks, and incomplete internal connection networks, hindering the abilities of classical compilation techniques in generating efficient code. We discuss a port of the Open Research Compiler (ORC) to such a complex processor, a new VLIW DSP, called the Parallel Architecture Core (PAC). We examine PAC, observe and characterize the machine models of such architectures, and how they are different from contemporary processors. We find that for such architectures, the restrictions on operand transport,
represents a new class of machine resource models. Such resource models are due to incomplete interconnection networks in the design, making structural properties emerge in such processors. Using the PAC processor as an example,
we describe a machine learning method that formulates the problem state space as a storage mapping of data operands,
and generate code for the PAC by doing combined instruction scheduling and operand storage assignment. We evaluate our algorithm using a benchmark suite for DSP processors, and find our technique to obtain approximately 35% to 40% improvement over a prior simplistic algorithm.

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