Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Proceedings of the joint conference on Languages, compilers and tools for embedded systems: software and compilers for embedded systems
System-level exploration for pareto-optimal configurations in parameterized systems-on-a-chip
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Algorithms for High-Level Synthesis
IEEE Design & Test
Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multi-objective design space exploration using genetic algorithms
Proceedings of the tenth international symposium on Hardware/software codesign
A Systematic Approach to Exploring Embedded System Architectures at Multiple Abstraction Levels
IEEE Transactions on Computers
Overview of the MPSoC design challenge
Proceedings of the 43rd annual Design Automation Conference
Interactive presentation: Soft-core processor customization using the design of experiments paradigm
Proceedings of the conference on Design, automation and test in Europe
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Efficient symbolic multi-objective design space exploration
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Signature-Based Calibration of Analytical System-Level Performance Models
SAMOS '08 Proceedings of the 8th international workshop on Embedded Computer Systems: Architectures, Modeling, and Simulation
Amdahl's Law in the Multicore Era
Computer
Discrete Particle Swarm Optimization for Multi-objective Design Space Exploration
DSD '08 Proceedings of the 2008 11th EUROMICRO Conference on Digital System Design Architectures, Methods and Tools
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A correlation-based design space exploration methodology for multi-processor systems-on-chip
Proceedings of the 47th Design Automation Conference
Decision-theoretic design space exploration of multiprocessor platforms
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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This article presents a detailed overview and the experimental comparison of 15 multi-objective design-space exploration (DSE) algorithms for high-level design. These algorithms are collected from recent literature and include heuristic, evolutionary, and statistical methods. To provide a fair comparison, the algorithms are classified according to the approach used and examined against a large set of metrics. In particular, the effectiveness of each algorithm was evaluated for the optimization of a multiprocessor platform, considering initial setup effort, rate of convergence, scalability, and quality of the resulting optimization. Our experiments are performed with statistical rigor, using a set of very diverse benchmark applications (a video converter, a parallel compression algorithm, and a fast Fourier transformation algorithm) to take a large spectrum of realistic workloads into account. Our results provide insights on the effort required to apply each algorithm to a target design space, the number of simulations it requires, its accuracy, and its precision. These insights are used to draw guidelines for the choice of DSE algorithms according to the type and size of design space to be optimized.