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- W4378895492 abstract "The advanced multi-core technology enables parallel computing to speed up Automatic Test Pattern Generation (ATPG). The main challenge is to solve an increasing number of hard-to-solve faults effectively. In this paper, we develop an efficient parallel computing system for the ATPG program, i.e., FPGNN-ATPG, which is consisted of two parts: graph-neural-networks-based (GNN-based) fault classification and fault-driven deterministic test pattern generator (DTPG). The end-to-end GNN-based classifier can predict fault types with superior accuracy compared with classical machine learning methods. And the fault-driven DTPG can solve different types of faults in parallel without runtime overhead. According to the experimental results on an 8-core machine, our FPGNN-ATPG framework obtains an average of 7.56X speedup while reducing 14.13% pattern count ratio with full 100% fault coverage for 10 industrial instances." @default.
- W4378895492 created "2023-06-01" @default.
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- W4378895492 date "2023-06-05" @default.
- W4378895492 modified "2023-09-27" @default.
- W4378895492 title "FPGNN-ATPG: An Efficient Fault Parallel Automatic Test Pattern Generator" @default.
- W4378895492 cites W2039413528 @default.
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- W4378895492 doi "https://doi.org/10.1145/3583781.3590250" @default.
- W4378895492 hasPublicationYear "2023" @default.
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