The nature of statistical learning theory
The nature of statistical learning theory
A study in coverage-driven test generation
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Causality based generation of directed test cases
ASP-DAC '00 Proceedings of the 2000 Asia and South Pacific Design Automation Conference
Hole analysis for functional coverage data
Proceedings of the 39th annual Design Automation Conference
Coverage Metrics for Functional Validation of Hardware Designs
IEEE Design & Test
Coverage-Directed Test Generation Using Symbolic Techniques
FMCAD '96 Proceedings of the First International Conference on Formal Methods in Computer-Aided Design
Cost evaluation of coverage directed test generation for the IBM mainframe
Proceedings of the IEEE International Test Conference 2001
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A Framework for Constrained Functional Verification
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Functional Coverage Driven Test Generation for Validation of Pipelined Processors
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Advanced analysis techniques for cross-product coverage
HLDVT '05 Proceedings of the High-Level Design Validation and Test Workshop, 2005. on Tenth IEEE International
A PD-based methodology to enhance efficiency in testbenches with random stimulation
Proceedings of the 22nd Annual Symposium on Integrated Circuits and System Design: Chip on the Dunes
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Journal of Electronic Testing: Theory and Applications
Coverage-Directed Test Generation Automated by Machine Learning -- A Review
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Online selection of effective functional test programs based on novelty detection
Proceedings of the International Conference on Computer-Aided Design
Novel test detection to improve simulation efficiency: a commercial experiment
Proceedings of the International Conference on Computer-Aided Design
Manipulation of Training Sets for Improving Data Mining Coverage-Driven Verification
Journal of Electronic Testing: Theory and Applications
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Extensive software-based simulation continues to be the mainstream methodology for functional verification of designs. To optimize the use of limited simulation resources, coverage metrics are essential to guide the development of effective test suites. Traditional coverage metrics are defined based on either a functional model or a structural model of the design. If our goal is to select a subset of tests from a set of tests, using these coverage metrics require simulation of the entire set before the effectiveness of tests can be compared. In this paper, we propose a novel methodology that estimates the input space covered by a set of tests. We use unsupervised support vector analysis to learn such a space, resulting in a subset of tests that represent the original set of tests. A direct application of this methodology is to select tests before simulation in order to reduce simulation cycles. Consequently, simulation effectiveness can be improved. Experimental results based on application of the proposed methodology to the OpenSparc T1 processor are reported to demonstrate the practicality of our approach.