An artificial intelligence approach to test generation
An artificial intelligence approach to test generation
Relative scheduling under timing constraints
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
MediaBench: a tool for evaluating and synthesizing multimedia and communicatons systems
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Surviving the SOC revolution: a guide to platform-based design
Surviving the SOC revolution: a guide to platform-based design
An Artificial Intelligence Approach to VLSI Design
An Artificial Intelligence Approach to VLSI Design
Using Feedback to Improve VLSI Designs
IEEE Expert: Intelligent Systems and Their Applications
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
A GA-based design space exploration framework for parameterized system-on-a-chip platforms
IEEE Transactions on Evolutionary Computation
Approximation accuracy analysis of fuzzy systems as function approximators
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Approximation Capabilities of Hierarchical Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A multiobjective genetic approach for system-level exploration in parameterized systems-on-a-chip
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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In this paper we present an intelligent approach for Computer Aided Design, that is capable to learn from its experience in order to speedup the design process. The proposed approach integrates two well known soft-computing techniques, Multi-Objective Genetic Algorithms (MOGAs) and Fuzzy Systems (FSs): MOGA smartly explores the design space, in the meanwhile the FS learn from the experience accumulated during the MOGA evolution, storing knowledge in fuzzy rules. The joined rules build the Knowledge Base through which the integrated system quickly predict the results of complex simulations thus avoiding their long execution times. The methodology is applied to a real case study and evaluated in terms of both efficiency and accuracy, demonstrating the superiority of the intelligent approach against brute force random search.