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dc.contributor.authorShen, Cheng-Hsienen_US
dc.contributor.authorLiang, Aaron C-Wen_US
dc.contributor.authorHsu, Charles C-Hen_US
dc.contributor.authorWen, Charles H-Pen_US
dc.date.accessioned2020-10-05T02:00:29Z-
dc.date.available2020-10-05T02:00:29Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-7281-4823-6en_US
dc.identifier.issn1089-3539en_US
dc.identifier.urihttp://hdl.handle.net/11536/155016-
dc.description.abstractAs the Register Transfer Level (RTL) designs are more complicated, debugging becomes a major bottleneck in the design process. To make debugging more efficient, failure binning aims at grouping failure traces caused by the same error source together so that designers can focus on one bug at one time. However, as there are multiple bugs in a design, behaviors exhibited by failure traces are diverse and severely confuse designers. One error source may result in different appearances subject to different activation conditions. In addition, different error sources may also exhibit similar appearances among the limited number of failure traces. In this work, we propose an autoencoder-based failure binning engine name FAE for debugging RTL designs more efficiently. The autoencoders extract meaningful representations from the sparse and high-dimensional feature space to the latent space with good properties for clustering. Superior to prior works, FAE provides confidence ranks between bins and in a bin to clearly guide designers during debugging. Experimental results show that FAE can drive bins of higher purity under an acceptable number of bins than prior works, dropping only few less-informative failures. Evaluated by three common metrics for clustering, FAE also achieves averagely 13.1% improvement in purity, 25.0% improvement in NMI and 18.2% improvement in ARI, respectively. As a result, the proposed autoencoder-based engine, FAE, applies machine learning to extract useful information from diverse failure traces and is effective on failure binning with more focused debugging.en_US
dc.language.isoen_USen_US
dc.subjectdebugen_US
dc.subjectRegister Transfer Level (RTL)en_US
dc.subjectfailure binningen_US
dc.subjectmachine learningen_US
dc.subjectautoencoderen_US
dc.subjectlatent spaceen_US
dc.titleFAE: Autoencoder-Based Failure Binning of RTL Designs for Verification and Debuggingen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2019 IEEE INTERNATIONAL TEST CONFERENCE (ITC)en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000540385000067en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper