C4.5: programs for machine learning
C4.5: programs for machine learning
MiBench: A free, commercially representative embedded benchmark suite
WWC '01 Proceedings of the Workload Characterization, 2001. WWC-4. 2001 IEEE International Workshop
Decision-theoretic exploration of multiProcessor platforms
CODES+ISSS '06 Proceedings of the 4th international conference on Hardware/software codesign and system synthesis
Adaptive compilation and inlining
Adaptive compilation and inlining
Efficient design space exploration for application specific systems-on-a-chip
Journal of Systems Architecture: the EUROMICRO Journal
Predictive design space exploration using genetically programmed response surfaces
Proceedings of the 45th annual Design Automation Conference
Efficient system design space exploration using machine learning techniques
Proceedings of the 45th annual Design Automation Conference
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
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Meta-model Assisted Optimization for Design Space Exploration of Multi-Processor Systems-on-Chip
DSD '09 Proceedings of the 2009 12th Euromicro Conference on Digital System Design, Architectures, Methods and Tools
Multi-processor system-on-chip design space exploration based on multi-level modeling techniques
SAMOS'09 Proceedings of the 9th international conference on Systems, architectures, modeling and simulation
A correlation-based design space exploration methodology for multi-processor systems-on-chip
Proceedings of the 47th Design Automation Conference
Reconfigurable Grid Alu Processor: Optimization and Design Space Exploration
DSD '10 Proceedings of the 2010 13th Euromicro Conference on Digital System Design: Architectures, Methods and Tools
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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During development, processor architectures can be tuned and configured by many different parameters. For benchmarking, automatic design space explorations (DSEs) with heuristic algorithms are a helpful approach to find the best settings for these parameters according to multiple objectives, e.g. performance, energy consumption, or real-time constraints. But if the setup is slightly changed and a new DSE has to be performed, it will start from scratch, resulting in very long evaluation times. To reduce the evaluation times we extend the NSGA-II algorithm in this article, such that automatic DSEs can be supported with a set of transformation rules defined in a highly readable format, the fuzzy control language (FCL). Rules can be specified by an engineer, thereby representing existing knowledge. Beyond this, a decision tree classifying high-quality configurations can be constructed automatically and translated into transformation rules. These can also be seen as very valuable result of a DSE because they allow drawing conclusions on the influence of parameters and describe regions of the design space with high density of good configurations. Our evaluations show that automatically generated decision trees can classify near optimal configurations for the hardware parameters of the Grid ALU Processor (GAP) and M-Sim 2. Further evaluations show that automatically constructed transformation rules can reduce the number of evaluations required to reach the same quality of results as without rules by 43%, leading to a significant saving of time of about 25%. In the demonstrated example using rules also leads to better results.