Embedding Hierarchical Signal to Siamese Network for Fast Name Rectification
Yi-An Chen1,a, Gung-Yu Pan2,d, Che-Hua Shih2,e, Yen-Chin Liao1,b, Chia-Chih Yen2,f and Hsie-Chia Chang1,c
1Department of Electronics Engineering, Institute of Electronics, National Chiao Tung University
aandychen.ee02@nctu.edu.tw
bycliaoee92g@gmail.com
chcchang@mail.nctu.edu.tw
2Synopsys Inc.
dgpan@synopsys.com
ematar@synopsys.com
fjackyen@synopsys.com
ABSTRACT
EDA tools are necessary to assist complicated flow of advanced IC design and verification in nowadays industry. After synthesis or simulation, the same signal could be viewed as different hierarchical names, especially for mixed-language designs. This name mismatching problem blocks automation and needs experienced users to rectify manually with domain knowledge. Even rule-based rectification helps the process but still fails when encountering unseen mismatching types. In this paper, hierarchical name rectification is transformed into the similarity search problem where the most similar name becomes the rectified name. However, naive full search in design with string comparison costs unacceptable time. Our proposed framework embeds name strings into vectors for representing distance relation in a latent space using character n-gram and locality-sensitive hashing (LSH), and then finds the most similar signal using nearest neighbor search (NNS) and detailed search. Learning similarity using Siamese network provides general name rectification regardless of mismatching types, while stringto- vector embedding for proximity search accelerates the rectification process. Our approach is capable of achieving 93.43% rectification rate with only 0.052s per signal, which outperforms the naive string search with 2.3% higher accuracy and 4,500 times speed-up
Keywords: Hierarchical name Rectification, Similarity Learning, Nearest Neighbor Search, Locality-sensitive Hashing, Embedding, Siamese Network.
