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Priority Directed Test Generation for Functional Verification Using Neural Networks by Hao Shen; Yuzhuo Fu is a scholarly article available to read on EtoBox.

What is Priority Directed Test Generation for Functional Verification Using Neural Networks about?

Functional verification is the bottleneck in delivering today's highly integrated electronic systems and chips. We should notice the simulation times and computation resource challenge in the automatic pseudo-random test generation and a novel solution named Priority Directed test Generation (PDG) is proposed in this paper. With PDG, a test vector which hasn't been simulated is granted a priority attribute. The priority indicates the possibility of detecting new bugs by simulating this vector. We show how to apply Artificial Neural Networks (ANNs) learning algorithm to the PDG problem. Several experiments are given to exhibit how to achieve better result in this PDG method.

Author

Hao Shen; Yuzhuo Fu

Publisher

ACM Press

Published

2005

Language

EN