Memory Allocation Problems in Embedded Systems

190 Pages·2012·2.18 MB·English

Memory Allocation Problems in Embedded Systems
Memory Allocation Problems
in
Embedded Systems
Optimization Methods
María Soto
André Rossi
Marc Sevaux
Johann Laurent
Series Editor
Narendra Jussien
Firstpublished2013inGreatBritainandtheUnitedStatesbyISTELtdandJohnWiley&Sons,Inc.
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Table of Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . ix
Chapter 1. Context . . . . . . . . . . . . . . . . . . . . . . 1
1.1. Embedded systems . . . . . . . . . . . . . . . . . . 2
1.1.1. Main components of embedded systems . . 3
1.2. Memory management for decreasing power
consumption, performance and area in
embedded systems . . . . . . . . . . . . . . . . . . 4
1.3. State of the art in optimization techniques for
memory management and data assignment . . 8
1.3.1. Software optimization . . . . . . . . . . . . . 9
1.3.2. Hardware optimization . . . . . . . . . . . . 11
1.3.3. Data binding . . . . . . . . . . . . . . . . . . . 16
1.3.3.1. Memory partitioning problem for low
energy . . . . . . . . . . . . . . . . . . . . . 17
1.3.3.2. Constraintsonmemorybankcapacities
and number of accesses to variables . . 18
1.3.3.3. Using external memory . . . . . . . . . . 19
1.4. Operations research and electronics . . . . . . . 21
1.4.1. Main challenges in applying operations
research to electronics . . . . . . . . . . . . . 23
vi MemoryAllocationProblemsinEmbeddedSystems
Chapter 2. Unconstrained Memory Allocation
Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . 28
2.2. An ILP formulation for the unconstrained
memory allocation problem . . . . . . . . . . . . . 31
2.3. Memory allocation and the chromatic number . 32
2.3.1. Bounds on the chromatic number . . . . . . 33
2.4. An illustrative example . . . . . . . . . . . . . . . 35
2.5. Three new upper bounds on the chromatic
number . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.6. Theoretical assessment of three
upper bounds . . . . . . . . . . . . . . . . . . . . . 45
2.7. Computational assessment of three
upper bounds . . . . . . . . . . . . . . . . . . . . . 49
2.8. Conclusion . . . . . . . . . . . . . . . . . . . . . . . 53
Chapter 3. Memory Allocation Problem With
Constraint on the Number of Memory Banks . . . 57
3.1. Introduction . . . . . . . . . . . . . . . . . . . . . . 58
3.2. An ILP formulation for the memory allocation
problem with constraint on the number of
memory banks . . . . . . . . . . . . . . . . . . . . . 61
3.3. An illustrative example . . . . . . . . . . . . . . . 64
3.4. Proposed metaheuristics . . . . . . . . . . . . . . 65
3.4.1. A tabu search procedure . . . . . . . . . . . . 66
3.4.2. A memetic algorithm . . . . . . . . . . . . . . 69
3.5. Computational results and discussion . . . . . . 71
3.5.1. Instances . . . . . . . . . . . . . . . . . . . . . 72
3.5.2. Implementation . . . . . . . . . . . . . . . . . 72
3.5.3. Results . . . . . . . . . . . . . . . . . . . . . . 73
3.5.4. Discussion . . . . . . . . . . . . . . . . . . . . 75
3.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . 75
TableofContents vii
Chapter 4. General Memory
Allocation Problem . . . . . . . . . . . . . . . . . . . . . 77
4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . 78
4.2. ILP formulation for the general memory
allocation problem . . . . . . . . . . . . . . . . . . 80
4.3. An illustrative example . . . . . . . . . . . . . . . 84
4.4. Proposed metaheuristics . . . . . . . . . . . . . . 85
4.4.1. Generating initial solutions . . . . . . . . . . 86
4.4.1.1. Random initial solutions . . . . . . . . . 86
4.4.1.2. Greedy initial solutions . . . . . . . . . . 86
4.4.2. A tabu search procedure . . . . . . . . . . . . 89
4.4.3. Exploration of neighborhoods . . . . . . . . 91
4.4.4. A variable neighborhood search hybridized
with a tabu search . . . . . . . . . . . . . . . 93
4.5. Computational results and discussion . . . . . . 94
4.5.1. Instances used . . . . . . . . . . . . . . . . . . 95
4.5.2. Implementation . . . . . . . . . . . . . . . . . 95
4.5.3. Results . . . . . . . . . . . . . . . . . . . . . . 96
4.5.4. Discussion . . . . . . . . . . . . . . . . . . . . 97
4.5.5. Assessing TabuMemex . . . . . . . . . . . . . 101
4.6. Statistical analysis . . . . . . . . . . . . . . . . . . 105
4.6.1. Post hoc paired comparisons . . . . . . . . . 106
4.7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . 107
Chapter 5. Dynamic Memory
Allocation Problem . . . . . . . . . . . . . . . . . . . . . 109
5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . 110
5.2. ILP formulation for dynamic memory
allocation problem . . . . . . . . . . . . . . . . . . 113
5.3. An illustrative example . . . . . . . . . . . . . . . 116
5.4. Iterative metaheuristic approaches . . . . . . . . 119
5.4.1. Long-term approach . . . . . . . . . . . . . . 119
5.4.2. Short-term approach . . . . . . . . . . . . . . 122
5.5. Computational results and discussion . . . . . . 123
5.5.1. Results . . . . . . . . . . . . . . . . . . . . . . 124
viii MemoryAllocationProblemsinEmbeddedSystems
5.5.2. Discussion . . . . . . . . . . . . . . . . . . . . 125
5.6. Statistical analysis . . . . . . . . . . . . . . . . . . 128
5.6.1. Post hoc paired comparisons . . . . . . . . . 129
5.7. Conclusion . . . . . . . . . . . . . . . . . . . . . . . 130
Chapter 6. MemExplorer: Cases Studies . . . . . . . 131
6.1. The design flow . . . . . . . . . . . . . . . . . . . . 131
6.1.1. Architecture used . . . . . . . . . . . . . . . . 131
6.1.2. MemExplorer design flow . . . . . . . . . . . 132
6.1.3. Memory conflict graph . . . . . . . . . . . . . 134
6.2. Example of MemExplorer utilization . . . . . . . 139
Chapter 7. General Conclusions and Future Work 147
7.1. Summary of the memory allocation
problem versions . . . . . . . . . . . . . . . . . . . 147
7.2. Intensification and diversification. . . . . . . . . 149
7.2.1. Metaheuristics for memory allocation
problem with constraint on the number of
memory banks . . . . . . . . . . . . . . . . . . 149
7.2.1.1. Tabu-Allocation . . . . . . . . . . . . . . . 149
7.2.1.2. Evo-Allocation . . . . . . . . . . . . . . . . 151
7.2.2. Metaheuristic for general memory
allocation problem . . . . . . . . . . . . . . . 151
7.2.3. Approaches for dynamic memory
allocation problem . . . . . . . . . . . . . . . 152
7.3. Conclusions . . . . . . . . . . . . . . . . . . . . . . 152
7.4. Future work . . . . . . . . . . . . . . . . . . . . . . 154
7.4.1. Theoretical perspectives . . . . . . . . . . . . 154
7.4.2. Practical perspectives . . . . . . . . . . . . . 156
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . 159
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Introduction
This book addresses four memory allocation problems.
The following sections present the motivations, the main
contributions and the outline of this book.
Motivations
Embedded systems are ever present in contemporary
society and they 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, telecommunication, and flight control; they are also
playing an important role in our daily activities (e.g.
smartphones, security alarms and traffic lights).
The significant development in embedded systems is
mainly due to advances in nano technology. These continuous
advances have made possible the design of miniaturized
electronic chips, leading to drastically extend the features
supported by embedded systems. Smartphones that can surf
the Web and process HD images are a typical example. In
addition to market pressure, this context has favored the
development of computer-aided design (CAD) software, which
brings a greater change to the designer’s line of work. While
x MemoryAllocationProblemsinEmbeddedSystems
technology offers more and more opportunities, the design of
embedded systems becomes more and more complex. Indeed,
the design of an integrated circuit, whose size is calculated
inbillionsoftransistors,thousandsofmemories,etc.,requires
theuseofcompetitivecomputertools.Thesetoolshavetosolve
optimization problems to ensure a low cost in terms of area
and time, and they must meet some standards in electronics.
Currently, in the electronics industry, the problems are
often addressed using either ad hoc methods based on
the designer expertise or general methods (typically genetic
algorithms). But both methods do not work well in solving
large-scale industrial problems.
Ontheotherhand,computer-aideddesignsoftwaresuchas
Gaut [GAU 93, COU 06] has been developed to generate the
architecture of a chip (circuit) from its specifications. While
the design process is significantly faster with these types
of software, the generated layouts are considered to be poor
on power consumption and surface compared to man-made
expertly-designed circuits. This is a major drawback as
embedded products have to feature low-power consumption.
In the design of embedded systems, memory allocation
and data assignment are among the main challenges that
electronic designers have to face. Indeed, they deeply
impact on the main cost metrics (power consumption,
performance and area) in electronic devices [WUY 96].
Thus, designers of embedded system have to carefully
pay attention to minimize memory requirements, 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.
Moreover,theneedforoptimizationoftheallocationofdata
structures is expected to become even more stringent in the
Introduction xi
future,asembeddedsystemswillrunheavycomputations.As
anexample,somecellphonesalreadysupportmulti-threading
operating systems.
For these reasons, we are interested in the allocation of
data structures into memory banks. This problem is rather
difficult to handle and is often left to the compiler with
which automatic rules are applied. Nevertheless, an optimal
allocation of data to memory banks may lead to greater
savings in terms of running time and energy consumption.
As has often been observed in microelectronics, this
complex problem is poorly modeled or not modeled at all.
The proposed solutions are based on a lower modeling level
that often only considers one objective at a time. Also, the
optimization of methods is little (or not) quantified, only the
running time is available and assessed. Thus, the models and
data are not analyzed much.
In this book, we model this problem and propose
optimization methods from operations research for
addressing it.
Contribution
In memory management and data assignment, there is an
abundant literature on the techniques for optimizing source
code and for designing a good architecture for an application.
However, not much work looks at finding a good allocation of
datastructureto memorybanks.Hence,thefirst contribution
of this book is the introduction of four versions of memory
allocation problems, which are either related to designing
the memory architecture or focused on the data structure
assignment.
The second important contribution of this book is the
introduction of three new upper bounds on the chromatic