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Optimizing system-on-chip verifications with multi-objective genetic evolutionary algorithms

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  • Verification of semiconductor chip designs is commonly driven by single goal orientated measures. With increasing design complexities, this approach is no longer effective. We enhance the effectiveness of coverage driven design verifications by applying multi-objective optimization techniques. The technique is based on genetic evolutionary algorithms. Difficulties with conflicting test objectives and selection of tests to achieve multiple verification goals in the genetic evolutionary framework are also addressed.
    Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.


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