To demonstrate its effectiveness, document-extracted features are employed to learn the rules for covering a set of assertions based on a commercial SoC.
Feature Extraction from Design Documents to Enable Rule Learning for Improving Assertion Coverage. Kuo-Kai Hsieh, Sebastian Siatkowski, Li-C. Wang.
Abstract— Feature selection is essential to rule learning in the context of functional verification. In practice today, features are selected manually and ...
Request PDF | On Jan 1, 2017, Kuo-Kai Hsieh and others published Feature extraction from design documents to enable rule learning for improving assertion ...
ASP-DAC 2017, Feature extraction from design documents to enable rule learning for improving assertion coverage Paper PDF IEEEXplore Link. DAC 2017, Learning ...
Feature extraction from design documents to enable rule learning for improving assertion coverage. ASP-DAC 2017: 51-56. [c5]. view. electronic edition via DOI ...
Li-C. Wang's research works | University of California, Santa ...
www.researchgate.net › publications
Machine Learning for Feature-Based Analytics · Feature extraction from design documents to enable rule learning for improving assertion coverage. Citing ...
Feature extraction from design documents to enable rule learning for improving assertion coverage · Kuo-Kai HsiehSebastian SiatkowskiLi-C. WangWen ChenJ ...
FEATURE EXTRACTION FROM DESIGN DOCUMENTS TO ENABLE RULE LEARNING FOR. IMPROVING ASSERTION COVERAGE ... USING SEGMENTATION TO IMPROVE SCHEDULABILITY OF RRA ...
Feature extraction from design documents to enable rule learning for improving assertion coverage. In Proceedings of the 22nd Asia and South Pacific Design ...