Simulation data mining for functional test pattern justification

Charles H.P. Wen*, Li C. Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In simulation-based functional verification, composing and debugging testbenches can be tedious and timeconsuming. A simulation-based data-mining approach [3] was proposed as an alternative for functional test pattern generation. However, the core of the approach is in solving Boolean learning, which is the problem of learning Boolean functions from bit-level simulation data. In this paper, an efficient data mining engine based on novel decision-diagram(DD) based learning approaches is presented. We compare the DD-based learning approaches to other known methods such as Nearest Neighbor and Support Vector Machine. We show that the new Boolean data miner is efficient for practical use and the learned results can provide compact and accurately approximate representations of Boolean functions. Finally, we show that the proposed methodology incorporated with the current Boolean data miner can achieve a high fault coverage (95.36%) on the OpenRISC 1200 microprocessor, demonstrating the effectiveness of our approach.

Original languageEnglish
Title of host publicationProceedings - Sixth International Workshop on Microprocessor Test and Verification
Subtitle of host publicationCommon Challenges and Solutions, MTV 2005
Pages76-83
Number of pages8
DOIs
StatePublished - 1 Dec 2006
Event2005 6th International Workshop on Microprocessor Test and Verification - Austin, TX, United States
Duration: 3 Nov 20054 Nov 2005

Publication series

NameProceedings - International Workshop on Microprocessor Test and Verification
ISSN (Print)1550-4093

Conference

Conference2005 6th International Workshop on Microprocessor Test and Verification
CountryUnited States
CityAustin, TX
Period3/11/054/11/05

Fingerprint Dive into the research topics of 'Simulation data mining for functional test pattern justification'. Together they form a unique fingerprint.

Cite this