| 研究生: |
羅仕喬 Lo, Shih-Chaio |
|---|---|
| 論文名稱: |
利用基因演算法進行單晶片網路架構之預設式備份路徑的容錯通訊 Genetic Algorithm-based Preplanned Backup Paths for Network-on-Chip Fault-Tolerant Communication |
| 指導教授: |
王億富
Wang, Yih-Fuh 黃振發 Huang, Jen-Fa |
| 學位類別: |
碩士 Master |
| 系所名稱: |
電機資訊學院 - 電機工程學系 Department of Electrical Engineering |
| 論文出版年: | 2004 |
| 畢業學年度: | 92 |
| 語文別: | 英文 |
| 論文頁數: | 65 |
| 中文關鍵詞: | 修復 、基因演算法 、預設式備份路徑 、單晶片網路 |
| 外文關鍵詞: | genetic algorithm, restoration, preplanned backup path, Network-on-Chip |
| 相關次數: | 點閱:207 下載:2 |
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在單晶片網路架構中,任意節點或線路的損壞,都可能造成資料傳輸的困難。這些錯誤和損壞將很難被預測及避免。於是,逐步發展中的深次微米技術則建議單晶片網路應具有內建式容錯修復之能力。為了確保單晶片網路能維持正常運作,我們提出預設式備份路徑修復之機制,並針對此問題提出一些有效的解決方法。
為了有更好的修復成果,我們先利用基因演算法(Genetic Algorithm,GA)-一種基於自然選擇過程的一種最佳化搜尋機制,來追求效能的最佳化。基因演算法已經被廣泛應用於解決複雜的最佳化問題。然而,傳統的基因演算法並非毫無缺點;收斂速度慢及不能保證一定可以收斂到最佳解為基因演算法二項主要缺點,現今已有許多改良式基因演算法來加強執行的效能。
考慮到基因演算法的諸多缺點,我們提出壓縮映射基因演算法(Contract Mapping Genetic Algorithm, CMGA)來搜尋最佳備用路徑。模擬的結果顯示,該演算法不但具有較強的廣域搜索能力,而且能有效地解決過早收斂的問題。另外,對於一般化的基因演算法以及啟發式搜尋演算法也有較好的搜尋能力表現。
Failure of nodes or links in Network-on-Chip (NoC) may cause difficulties in data transmission. And these failures are hard to predict and avoid with current design methodologies. The demands of evolving deep-submicron VLSI technology suggest that Networks-on-Chips be designed with built-in fault-tolerance. In order to ensure Networks-on-Chips survivability for maintaining the normal operation, our proposed method is to implement the preplanned backup path restoration scheme.
We have presented a new method based upon a GA to find the near optimal preplanned backup paths for the primary paths in NoC. In this thesis, GA has been employed for solving many complex optimization problems. Although GA is not perfect, i.e., the speed of convergence is slow and it cannot guarantee to converge to globally optimal solution, they are more efficient in attaining near-optimal solutions significantly than conventional point-by-point exhaustive search techniques, especially in large solution spaces.
Taking the drawbacks of SGA into account, we adopt another genetic algorithm -- Contract Mapping Genetic Algorithm (CMGA) is proposed. Not only this algorithm can converge to globally optimal solution, but also it solves premature convergence problem efficiently. Simulation results show that the algorithm CMGA presented in this thesis performs better contrast with simple genetic algorithm and heuristic search algorithm.
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