Introduction |
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ix | |
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1 | (26) |
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2 | (2) |
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1.1.1 Main components of embedded systems |
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3 | (1) |
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1.2 Memory management for decreasing power consumption, performance and area in embedded systems |
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4 | (4) |
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1.3 State of the art in optimization techniques for memory management and data assignment |
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8 | (13) |
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1.3.1 Software optimization |
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9 | (2) |
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1.3.2 Hardware optimization |
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11 | (5) |
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16 | (1) |
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1.3.3.1 Memory partitioning problem for low energy |
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17 | (1) |
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1.3.3.2 Constraints on memory bank capacities and number of accesses to variables |
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18 | (1) |
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1.3.3.3 Using external memory |
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19 | (2) |
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1.4 Operations research and electronics |
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21 | (6) |
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1.4.1 Main challenges in applying operations research to electronics |
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23 | (4) |
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Chapter 2 Unconstrained Memory Allocation Problem |
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27 | (30) |
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28 | (3) |
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2.2 An ILP formulation for the unconstrained memory allocation problem |
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31 | (1) |
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2.3 Memory allocation and the chromatic number |
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32 | (3) |
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2.3.1 Bounds on the chromatic number |
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33 | (2) |
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2.4 An illustrative example |
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35 | (3) |
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2.5 Three new upper bounds on the chromatic number |
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38 | (7) |
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2.6 Theoretical assessment of three upper bounds |
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45 | (4) |
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2.7 Computational assessment of three upper bounds |
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49 | (4) |
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53 | (4) |
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Chapter 3 Memory Allocation Problem With Constraint on the Number of Memory Banks |
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57 | (20) |
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58 | (3) |
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3.2 An ILP formulation for the memory allocation problem with constraint on the number of memory banks |
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61 | (3) |
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3.3 An illustrative example |
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64 | (1) |
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3.4 Proposed metaheuristics |
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65 | (6) |
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3.4.1 A tabu search procedure |
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66 | (3) |
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3.4.2 A memetic algorithm |
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69 | (2) |
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3.5 Computational results and discussion |
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71 | (4) |
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72 | (1) |
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72 | (1) |
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73 | (2) |
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75 | (1) |
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75 | (2) |
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Chapter 4 General Memory Allocation Problem |
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77 | (32) |
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78 | (2) |
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4.2 ILP formulation for the general memory allocation problem |
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80 | (4) |
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4.3 An illustrative example |
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84 | (1) |
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4.4 Proposed metaheuristics |
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85 | (9) |
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4.4.1 Generating initial solutions |
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86 | (1) |
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4.4.1.1 Random initial solutions |
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86 | (1) |
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4.4.1.2 Greedy initial solutions |
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86 | (3) |
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4.4.2 A tabu search procedure |
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89 | (2) |
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4.4.3 Exploration of neighborhoods |
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91 | (2) |
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4.4.4 A variable neighborhood search hybridized with a tabu search |
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93 | (1) |
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4.5 Computational results and discussion |
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94 | (11) |
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95 | (1) |
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95 | (1) |
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96 | (1) |
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97 | (4) |
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4.5.5 Assessing TabuMemex |
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101 | (4) |
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105 | (2) |
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4.6.1 Post hoc paired comparisons |
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106 | (1) |
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107 | (2) |
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Chapter 5 Dynamic Memory Allocation Problem |
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109 | (22) |
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110 | (3) |
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5.2 ILP formulation for dynamic memory allocation problem |
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113 | (3) |
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5.3 An illustrative example |
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116 | (3) |
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5.4 Iterative metaheuristic approaches |
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119 | (4) |
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119 | (3) |
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5.4.2 Short-term approach |
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122 | (1) |
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5.5 Computational results and discussion |
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123 | (5) |
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124 | (1) |
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125 | (3) |
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128 | (2) |
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5.6.1 Post hoc paired comparisons |
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129 | (1) |
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130 | (1) |
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Chapter 6 MemExplorer: Cases Studies |
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131 | (16) |
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131 | (8) |
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131 | (1) |
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6.1.2 MemExplorer design flow |
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132 | (2) |
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6.1.3 Memory conflict graph |
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134 | (5) |
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6.2 Example of MemExplorer utilization |
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139 | (8) |
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Chapter 7 General Conclusions and Future Work |
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147 | (12) |
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7.1 Summary of the memory allocation problem versions |
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147 | (2) |
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7.2 Intensification and diversification |
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149 | (3) |
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7.2.1 Metaheuristics for memory allocation problem with constraint on the number of memory banks |
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149 | (1) |
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149 | (2) |
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151 | (1) |
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7.2.2 Metaheuristic for general memory allocation problem |
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151 | (1) |
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7.2.3 Approaches for dynamic memory allocation problem |
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152 | (1) |
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152 | (2) |
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154 | (5) |
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7.4.1 Theoretical perspectives |
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154 | (2) |
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7.4.2 Practical perspectives |
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156 | (3) |
Bibliography |
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159 | (22) |
Index |
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181 | |