Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Optimizing system-on-chip verifications with multi-objective genetic evolutionary algorithms|
|Citation:||Journal of Industrial and Management Optimization, 2014; 10(2):383-396|
|Publisher:||American Institute of Mathematical Sciences|
|Adriel Cheng and Cheng-Chew Lim|
|Abstract:||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.|
|Keywords:||Multi-objective optimization; genetic evolutionary algorithms; Pareto optimization; system-on-chip verification; coverage driven verification.|
|Rights:||Copyright status unknown|
|Appears in Collections:||Electrical and Electronic Engineering publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.