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Information Technology Journal

Year: 2012 | Volume: 11 | Issue: 12 | Page No.: 1786-1789
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Authors

Jin Shang

Liyong Zhang

Keywords

Research Article

Ant Colony Algorithm and Genetic Algorithm Optimization for Test Vector Reordering

Jin Shang and Liyong Zhang
Power dissipation during testing has been found to be much more than during normal mode due to increased switching activity. Test vector reordering technique helps mitigate this problem as it enables the reduction of switching activity during testing. This study presents a new test vector reordering approach. This approach maps the reordering problem to Traveling Salesman Problem (TSP). A hybrid ant colony algorithm and genetic algorithm is presented to solve this problem. The proposed approach is effective and verified with ISCAS’85 benchmark circuits which shows that yield on an average of about 30.05% reduction in switching activity.
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