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研究生:張巽翔
研究生(外文):Hsun-Hsiang Chang
論文名稱:運用多目標基因演算法於多模式系統之節省能量消耗匯流排通訊架構探勘
論文名稱(外文):Bus-based Communication Architecture Exploration for Energy-Aware Multi-Mode Systems using Multi-Objective Genetic Algorithm
指導教授:邱瀝毅
指導教授(外文):Lih-yih Chiou
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:82
中文關鍵詞:多模式系統匯流排通訊架構探勘效能與功率消耗
外文關鍵詞:power and performance trade-offmulti-mode systemsystem-on-chipdesign space explorationon-chip communication architecture
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為了因應多元化的使用需求,整合多種應用如H.264、MPEG等多媒體壓縮技術於同一晶片與多格式矽智財(IP)的開發越來越被重視。在以匯流排通訊架構為主的設計中,矽智財間的資料傳輸與溝通將會成為影響整體效能與功率消耗的關鍵。我們運用多目標基因演算法提出了一個可同時考量多種模式的快速匯流排通訊架構探勘方式,提出的演算法不僅可以考量各個模式的執行效能,也能進行功率消耗的最佳化。在實驗結果中,提出的演算法可找到的匯流排通訊架構離全域最佳解只有7%差距,同時也增快了多模式在利用基因演算法進行匯流排架構探勘時的效率,不必犧牲解的品質就能減少至多59%的探勘時間。
Integrating various applications such as H.264, MPEG etc. into a chip and forming to a multi-mode IP are increasingly important for meeting versatile consumers’ demands. In bus-based design, data transfer among the communication architecture may become the bottleneck of system performance and power consumption. We proposed an efficient bus-based communication architecture exploration method for a multi-mode system using a multi-objective genetic algorithm. The proposed method can explore communication architectures that not only meet performance constraints for all modes but also optimize the power on system communication. Experimental results indicate the proposed method is close to the optimal solution within 7%, we also overcome the inefficient searching in the genetic algorithm and obtain time improvement by up to 59% without compromising the quality of the solution.
Chapter 1 Introduction 1
1.1 Motivation 1
1.1.1 Integration of Multi-Standard and Multi-Mode for Multimedia Applications 1
1.1.2 Multimedia Systems Requirements 2
1.1.3 Impacts of On-Chip Communication Architecture 2
1.1.4 Benefit for Early Design 5
1.2 Problems of Communication Architecture Exploration under Multi-Mode 6
1.2.1 Mode-Balanced Communication Architecture 7
1.2.2 Multi-Mode Communication Architecture Trade-Off 9
1.3 Contributions 11
1.4 Thesis Organization 11
Chapter 2 Background 12
2.1 Concept of Multi-Mode and Multi-Standard System 12
2.1.1 Multi-Mode or Multi-Standard on Single IP 14
2.1.2 Multi-Mode or Multi-Standard Systems 15
2.2 Multi-objective Genetic Algorithm 17
2.3 Nonfunctional Performance and Power Estimation 20
Chapter 3 Related Work 24
3.1 Performance-driven approaches for system-level communication architectures 24
3.1.1 Automated throughput-driven synthesis of bus-based communication architectures 24
3.1.2 Design space exploration for optimizing on-chip communication architectures 27
3.2 Energy and Performance-aware on-chip communication architecture exploration and synthesis approaches 30
3.2.1 Simultaneous Partitioning and Frequency Assignment for On-Chip Bus Architectures 30
3.2.2 System Level Bus-Based Communication Architecture Exploration for Power and Performance Using Modified Simulated Annealing Algorithm 32
3.3 Summary 34
Chapter 4 Proposed Bus-based Communication Architecture Exploration for Multi-mode Systems (BAEM) 36
4.1 Problem Definition 36
4.2 Bus-based Communication Architecture Exploration for Multi-mode Systems (BAEM) 39
4.3 Chromosome Mapping 41
4.3.1 Genes for Components 43
4.3.2 Genes for Buses 44
4.3.3 Impacts of Gene Variation 44
4.4 Weighting Function 50
4.4.1 Hard Constraint Weighting Function (HCWF) 50
4.4.2 Soft Constraint Weighting Function (SCWF) 53
Chapter 5 Experimental Results 58
5.1 Experiment 1: Effectiveness of BAEM 58
5.1.1 Testcases 58
5.1.2 Results and Discussions 60
5.2 Experiment 2: Comparison between HCWF and SCWF 62
5.2.1 Experiment 2-1: Architecture Exploration under Tight Performance Constraints 62
5.2.1.1 Testcases 62
5.2.1.2 Results and Discussions 63
5.2.2 Experiment 2-2: Multiple Performance Constraints 64
5.2.2.1 Testcases 64
5.2.2.2 Results and Discussions 65
5.2.3 Experiment 2-3: Multiple GA Parameter Sets 67
5.2.3.1 Testcases 67
5.2.3.2 Results and Discussions 67
5.3 Experiment 3: Multi-Mode System - H.264 Encoder and Decoder 69
5.3.1 Testcases 69
5.3.2 Results and Discussions 72
Chapter 6 Conclusions and Future Work 74
6.1 Conclusions 74
6.2 Future Works 74
Reference 76
Appendix - The Source and the Parameter Settings for Multi-Objective Genetic Algorithm 80
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