System-Level Synthesis Using Evolutionary Algorithms
01 Jan 1998-Design Automation for Embedded Systems (Kluwer Academic Publishers)-Vol. 3, Iss: 1, pp 23-58
TL;DR: A model is introduced that handles all mentioned requirements and allows the task of system-synthesis to be specified as an optimization problem and the application and adaptation of an Evolutionary Algorithm to solve the tasks of optimization and design space exploration.
Abstract: In this paper, we consider system-level synthesis as the problem of optimally mapping a task-level specification onto a heterogeneous hardware/software architecture. This problem requires (1) the selection of the architecture (allocation) including general purpose and dedicated processors, ASICs, busses and memories, (2) the mapping of the specification onto the selected architecture in space (binding) and time (scheduling), and (3) the design space exploration with the goal to find a set of implementations that satisfy a number of constraints on cost and performance. Existing methodologies often consider a fixed architecture, perform the binding only, do not reflect the tight interdependency between binding and scheduling, do not consider communication (tasks and resources), or require long run-times preventing design space exploration, or yield only one implementation with optimal cost. Here, a model is introduced that handles all mentioned requirements and allows the task of system-synthesis to be specified as an optimization problem. The application and adaptation of an Evolutionary Algorithm to solve the tasks of optimization and design space exploration is described.
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TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Abstract: Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.
7,512 citations
Book•
30 Jun 2002
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
Abstract: List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.
5,994 citations
Book•
27 Dec 1999
TL;DR: The basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective and the focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms.
Abstract: Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making. In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Finally, two applications will presented and some recent trends in the field will be outlined.
2,062 citations
DOI•
01 Jan 1998
TL;DR: A new evolutionary approach to multicriteria optimization the Strength Pareto Evolutionary Algorithm SPEA is proposed which combines various features of previous multiobjective EAs in a unique manner and is characterized as follows.
Abstract: Evolutionary algorithms EA have proved to be well suited for optimization prob lems with multiple objectives Due to their inherent parallelism they are able to capture a number of solutions concurrently in a single run In this report we propose a new evolutionary approach to multicriteria optimization the Strength Pareto Evolutionary Algorithm SPEA It combines various features of previous multiobjective EAs in a unique manner and is characterized as follows a besides the population a set of individuals is maintained which contains the Pareto optimal solutions generated so far b this set is used to evaluate the tness of an individual according to the Pareto dominance relationship c unlike the commonly used tness sharing population diversity is preserved on basis of Pareto dominance rather than distance d a clustering method is incorporated to reduce the Pareto set without destroying its characteristics The proof of principle results on two problems suggest that SPEA is very e ective in sampling from along the entire Pareto optimal front and distributing the generated solutions over the tradeo surface Moreover we compare SPEA with four other multiobjective EAs as well as a single objective EA and a random search method in application to an extended knapsack problem Regarding two complementary quantitative measures SPEA outperforms the other approaches at a wide margin on this test problem Finally a number of suggestions for extension and application of the new algorithm are discussed
788 citations
Cites background or methods from "System-Level Synthesis Using Evolut..."
...According to our knowledge other niching techniques like crowding [De Jong,1975] and its derivatives have hardly ever been applied to EAs with multiple objec-tives (an exception is Blickle's EA [Blickle, 1996], cf. Section 4.2)....
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...The synthesis of a video codec, based on the H.261 standard(cf. [Blickle, 1996, Chapter 9]), was chosen as test problem; the search space of thisproblem contains about 1:9 1027 possible bindings....
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...…tasks are mapped to theresources with respect to the repaired allocation; further lists, permutations of theset of resources, de ne for each task separately which resource is checked next tomap the task to.11Again, interested readers are referred to [Blickle, 1996] for more detailed information....
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...[Blickle, 1996] Tobias Blickle....
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...The outcomes concerning the Pareto solutions found bythe single-objective EA are taken from [Blickle, 1996, p. 203]....
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TL;DR: The Sesame framework as mentioned in this paper provides high-level modeling and simulation methods and tools for system-level performance evaluation and exploration of heterogeneous embedded systems, and it takes a designer systematically along the path from selecting candidate architectures, using analytical modeling and multi-objective optimization, to simulating these candidate architectures with our system level simulation environment.
Abstract: The sheer complexity of today's embedded systems forces designers to start with modeling and simulating system components and their interactions in the very early design stages. It is therefore imperative to have good tools for exploring a wide range of design choices, especially during the early design stages, where the design space is at its largest. This paper presents an overview of the Sesame framework, which provides high-level modeling and simulation methods and tools for system-level performance evaluation and exploration of heterogeneous embedded systems. More specifically, we describe Sesame's modeling methodology and trajectory. It takes a designer systematically along the path from selecting candidate architectures, using analytical modeling and multiobjective optimization, to simulating these candidate architectures with our system-level simulation environment. This simulation environment subsequently allows for architectural exploration at different levels of abstraction while maintaining high-level and architecture-independent application specifications. We illustrate all these aspects using a case study in which we traverse Sesame's exploration trajectory for a motion-JPEG encoder application.
366 citations
References
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Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797 citations
Book•
01 Jan 1994
TL;DR: This book covers techniques for synthesis and optimization of digital circuits at the architectural and logic levels, i.e., the generation of performance-and-or area-optimal circuits representations from models in hardware description languages.
Abstract: From the Publisher:
Synthesis and Optimization of Digital Circuits offers a modern, up-to-date look at computer-aided design (CAD) of very large-scale integration (VLSI) circuits. In particular, this book covers techniques for synthesis and optimization of digital circuits at the architectural and logic levels, i.e., the generation of performance-and/or area-optimal circuits representations from models in hardware description languages. The book provides a thorough explanation of synthesis and optimization algorithms accompanied by a sound mathematical formulation and a unified notation. The text covers the following topics: modern hardware description languages (e.g., VHDL, Verilog); architectural-level synthesis of data flow and control units, including algorithms for scheduling and resource binding; combinational logic optimization algorithms for two-level and multiple-level circuits; sequential logic optimization methods; and library binding techniques, including those applicable to FPGAs.
2,311 citations
Proceedings Article•
01 Jun 1989
2,164 citations
"System-Level Synthesis Using Evolut..." refers methods in this paper
...In particular, for the allocations αi uniform crossover is used [21] that randomly swaps a bit between two parents with a probability of 50%....
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TL;DR: The authors present a software-oriented approach to hardware-software partitioning which avoids restrictions on the software semantics as well as an iterative partitioning process based on hardware extraction controlled by a cost function.
Abstract: The authors present a software-oriented approach to hardware-software partitioning which avoids restrictions on the software semantics as well as an iterative partitioning process based on hardware extraction controlled by a cost function. This process is used in Cosyma, an experimental cosynthesis system for embedded controllers. As an example, the extraction of coprocessors for loops is demonstrated. Results are presented for several benchmark designs. >
644 citations
"System-Level Synthesis Using Evolut..." refers background or methods in this paper
...[9] present an approach that starts with an initial partition in software and...
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..., communicating sequential processes [4], [24], C-code [13] and extensions thereof [9] or finite-state-machine based specifications, e....
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..., [16] (one processor and custom hardware communicating via memory-mapped I/O), [9] (one RISC processor and one or more given custom blocks and predefined HW-modules that communicate by memory coupling using a single CSP type protocol), or [12] (one programmable component and multiple hardware modules communicating with each other using one system bus, the processor being the master)....
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Proceedings Article•
15 Jul 1995TL;DR: This paper researches the new technique's restriction on competition from the viewpoint of calculating probability distributions for its tournaments as well as its various niche takeover times and explores the future trajectory of multimodal GA research.
Abstract: This paper investigates a new technique for the solving of multimodal problems using genetic algorithms (GAs). The proposed technique, Restricted Tournament Selection, is based on the paradigm of local competition. The paper begins by discussing some of the drawbacks of using current multi-modal techniques. The paper then presents the new technique along with an analysis of a class of sets of solutions it preserves and locates. This presentation researches the new technique's restriction on competition from the viewpoint of calculating probability distributions for its tournaments as well as its various niche takeover times. Empirical observations are then presented as evidence of the technique's abilities in a wide variety of settings. Finally, this paper explores the future trajectory of multimodal GA research. Abstract This paper investigates a new technique for the solving of multimodal problems using genetic algorithms (GAs). The proposed technique, Restricted Tournament Selection, is based on the paradigm of local competition. The paper begins by discussing some of the drawbacks of using current multi-modal techniques. The paper then presents the new technique along with an analysis of a class of sets of solutions it preserves and locates. This presentation researches the new technique's restriction on competition from the viewpoint of calculating probability distributions for its tournaments as well as its various niche takeover times. Empirical observations are then presented as evidence of the technique's abilities in a wide variety of settings. Finally, this paper explores the future trajectory of multimodal GA research.
472 citations