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Memory Allocation Problems in Embedded Systems : Optimization Methods.
Embedded systems are everywhere in contemporary life and are supposed to make our lives more comfortable. In industry, embedded systems are used to manage and control complex systems (e.g. nuclear power plants, telecommunications and flight control) and they are also taking an important place in our...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Wiley,
2013.
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| Colección: | ISTE.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Title Page; Contents; Introduction; Chapter 1. Context; 1.1. Embedded systems; 1.1.1. Main components of embedded systems; 1.2. Memory management for decreasing power consumption; 1.3. State of the art in optimization techniques for memory management and data assignment; 1.3.1. Software optimization; 1.3.2. Hardware optimization; 1.3.3. Data binding; 1.3.3.1. Memory partitioning problem for low energy; 1.3.3.2. Constraints on memory bank capacities and number of accesses to variables; 1.3.3.3. Using external memory; 1.4. Operations research and electronics.
- 1.4.1. Main challenges in applying operations research to electronicsChapter 2. Unconstrained Memory Allocation Problem; 2.1. Introduction; 2.2. An ILP formulation for the unconstrained memory allocation problem; 2.3. Memory allocation and the chromatic number; 2.3.1. Bounds on the chromatic number; 2.4. An illustrative example; 2.5. Three new upper bounds on the chromatic number; 2.6. Theoretical assessment of three upper bounds; 2.7. Computational assessment of three upper bounds; 2.8. Conclusion; Chapter 3. Memory Allocation Problem With Constraint on the Number of Memory Banks.
- 3.1. Introduction3.2. An ILP formulation for the memory allocation problem with constraint on the number of memory banks; 3.3. An illustrative example; 3.4. Proposed metaheuristics; 3.4.1. A tabu search procedure; 3.4.2. A memetic algorithm; 3.5. Computational results and discussion; 3.5.1. Instances; 3.5.2. Implementation; 3.5.3. Results; 3.5.4. Discussion; 3.6. Conclusion; Chapter 4. General Memory Allocation Problem; 4.1. Introduction; 4.2. ILP formulation for the general memory allocation problem; 4.3. An illustrative example; 4.4. Proposed metaheuristics.
- 4.4.1. Generating initial solutions4.4.1.1. Random initial solutions; 4.4.1.2. Greedy initial solutions; 4.4.2. A tabu search procedure; 4.4.3. Exploration of neighborhoods; 4.4.4. A variable neighborhood search hybridized with a tabu search; 4.5. Computational results and discussion; 4.5.1. Instances used; 4.5.2. Implementation; 4.5.3. Results; 4.5.4. Discussion; 4.5.5. Assessing TabuMemex; 4.6. Statistical analysis; 4.6.1. Post hoc paired comparisons; 4.7. Conclusion; Chapter 5. Dynamic Memory Allocation Problem; 5.1. Introduction; 5.2. ILP formulation for dynamic memory allocation problem.
- 5.3. An illustrative example5.4. Iterative metaheuristic approaches; 5.4.1. Long-term approach; 5.4.2. Short-term approach; 5.5. Computational results and discussion; 5.5.1. Results; 5.5.2. Discussion; 5.6. Statistical analysis; 5.6.1. Post hoc paired comparisons; 5.7. Conclusion; Chapter 6. MemExplorer: Cases Studies; 6.1. The design flow; 6.1.1. Architecture used; 6.1.2. MemExplorer design flow; 6.1.3. Memory conflict graph; 6.2. Example of MemExplorer utilization; Chapter 7. General Conclusions and Future Work 147; 7.1. Summary of the memory allocation problem versions.