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E-raamat: Memory Allocation Problems in Embedded Systems / Optimization Methods: Optimization Methods [Wiley Online]

  • Formaat: 208 pages
  • Sari: ISTE
  • Ilmumisaeg: 14-Dec-2012
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1118577582
  • ISBN-13: 9781118577585
Teised raamatud teemal:
  • Wiley Online
  • Hind: 174,45 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Formaat: 208 pages
  • Sari: ISTE
  • Ilmumisaeg: 14-Dec-2012
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1118577582
  • ISBN-13: 9781118577585
Teised raamatud teemal:

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 daily activities (e.g. smartphones, security alarms and traffic lights).
In the design of embedded systems, memory allocation and data assignment are among the main challenges that electronic designers have to face. In fact, they impact heavily on the main cost metrics (power consumption, performance and area) in electronic devices. Thus designers of embedded systems have to pay careful attention in order to minimize memory requirements, thus improving memory throughput and limiting the power consumption by the system’s memory. Electronic designers attempt to minimize memory requirements with the aim of lowering the overall system costs.
A state of the art of optimization techniques for memory management and data assignment is presented in this book.

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

Marc Sevaux is the author of Memory Allocation Problems in Embedded Systems: Optimization Methods, published by Wiley.

André Rossi is the author of Memory Allocation Problems in Embedded Systems: Optimization Methods, published by Wiley.

Johann Laurent is the author of Memory Allocation Problems in Embedded Systems: Optimization Methods, published by Wiley.