GPU-based n-detect transition fault ATPG

  • Authors:
  • Kuan-Yu Liao;Sheng-Chang Hsu;James Chien-Mo Li

  • Affiliations:
  • National Taiwan University, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan;National Taiwan University, Taipei, Taiwan

  • Venue:
  • Proceedings of the 50th Annual Design Automation Conference
  • Year:
  • 2013

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Abstract

This is a massively parallel ATPG that explores device-level, block-level and word-level parallelism in GPU. Eight-detect transition fault ATPG experiments on large benchmark circuits show that our technique achieved 5.6 and 1.6 times speedup compared with a single-core and 8-core CPU commercial tool, respectively. Test patterns selected from our test set are about the same length and quality as those selected from commercial N-detect ATPG. To the best of our knowledge, this is the first proposed GPU-based ATPG algorithm.